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Results 251-500 of 3437 (3348 ASCL, 89 submitted)

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[ascl:2306.024] COpops: Compute CO sizes and fluxes

COpops computes semi-analytically the CO flux of a disc (given initial conditions and age) under the assumption of LTE and optically thick emission. It then runs disc population synthesis using observationally-informed initial conditions. CO fluxes is one of the most easily accessible observables for studying disc evolution; COpops is a faster alternative to running computationally-expensive thermochemical models for hundreds of discs and is accurate, recovering agreement within a factor of three.

[ascl:2306.023] RELAGN: AGN SEDs with full GR ray tracing

RELAGN creates spectral models for the calculation of AGN SEDs, ranging from the Optical/UV (outer accretion disc) to the Hard X-ray (Innermost X-ray Corona). The code is available in two languages, Python and Fortran. The Fortran version is written to be used with the spectral fitting software XSPEC (ascl:9910.005), and is the preferred version for analyzing X-ray spectral data. The Python version provides more flexibility for modeling. Whereas the Fortran version produces only a spectrum, the Python implementation can extract the physical properties of the system (such as the physical mass accretion rate, disc size, and efficiency parameters) since these are all stored as attributes within the model. Both versions require a working installation of HEASOFT (ascl:1408.004).

[ascl:2306.022] apollinaire: Helioseismic and asteroseismic peakbagging frameworks

apollinaire provides functions and a framework for helioseismic and asteroseismic instruments data managing and analysis, and includes all the tools necessary to analyze the acoustic oscillations of solar-like stars. The core of the package is the peakbagging library, which provides a full framework to extract oscillation modes parameters from solar and stellar power spectra.

[ascl:2306.021] pipes_vis: Interactive GUI and visualizer tool for SPS spectra

pipes_vis is an interactive graphical user interface for visualizing SPS spectra. Powered by Bagpipes (ascl:2104.017), it provides real-time manipulation of a model galaxy's star formation history, dust, and other relevant properties through sliders and text boxes.

[ascl:2306.020] mockFRBhosts: Limiting the visibility and follow-up of FRB host galaxies

mockFRBhosts estimates the fraction of FRB hosts that can be cataloged with redshifts by existing and future optical surveys. The package uses frbpoppy (ascl:1911.009) to generate a population of FRBs for a given radio telescope. For each FRB, a host galaxy is drawn from a data base generated by GALFORM (ascl:1510.005). The galaxies' magnitudes in different photometric surveys are calculated as are the number of bands in which they are detected. mockFRBhosts also calculates the follow-up time in a 10-m optical telescope required to do photometry or spectroscopy and provides a simple interface to Bayesian inference methods via MCMC simulations provided in the FRB package (ascl:2306.018).

[ascl:2306.019] realfast: Real-time interferometric data analysis for the VLA

The transient search pipeline realfast integrates with the real-time environment at the Very Large Array (VLA) to look for fast radio bursts, pulsars, and other rare astrophysical transients. The software monitors multicast messages, catches visibility data, and defines a fast transient search pipeline with rfpipe (ascl:1710.002). It indexes candidate transients and other metadata for the search interface, and writes and archives new visibility files for candidate transients. realfast provides support for GPU algorithms, manages distributed futures, and performs blind injection and management of mock transients, among other tasks, and rapidly distributes data products and transient alerts to the public.

[ascl:2306.018] FRB: Fast Radio Burst calculations, estimations, and analysis

FRB performs calculations, estimations, analysis, and Bayesian inferences for Fast Radio Bursts, including dispersion measure and emission measure calculations, derived properties and spectrums, and Galactic RM.

[ascl:2306.017] Zeus21: Simulations of 21-cm at cosmic dawn

Zeus21 (Zippy Early-Universe Solver for 21-cm) captures the nonlocal and nonlinear physics of cosmic dawn to create an effective model for the 21-cm power spectrum and global signal. The code takes advantage of the approximate log-normality of the star-formation rate density (SFRD) during cosmic dawn to compute the 21-cm power spectrum analytically. It agrees with more expensive semi-numerical simulations to roughly 10% precision, but has comparably negligible computational cost (~ s) and memory requirements. Zeus21 pairs well with data from HERA, but can be used for any 21-cm inference or prediction. Its capabilities include finding the 21-cm power spectrum (at a broad range of k and z), the global signal, IGM temperatures (Tk, Ts, Tcolor), neutral fraction xHI, Lyman-alpha fluxes, and the evolution of the SFRD; all across cosmic dawn z=5-35. It can also predict UVLFs for HST and JWST. Zeus21 can use three different astrophysical models, one of which emulates 21cmFAST (ascl:1102.023), and can vary the cosmology through CLASS (ascl:1106.020).

[ascl:2306.016] SuperRad: Black hole superradiance gravitational waveform modeler

SuperRad models ultralight boson clouds that arise through black hole superradiance. It uses numerical results in the relativistic regime combined with analytic estimates to describe the dynamics and gravitational wave signals of ultralight scalar or vector clouds. Written in Python, SuperRad includes a set of testing routines that check the internal consistency of the package; these tests mainly serve the purpose of ensuring functionality of the waveform model but can also be utilized to check that SuperRad works as intended.

[ascl:2306.015] Mangrove: Infer galaxy properties using dark matter merger trees

Mangrove uses Graph Neural Networks to regress baryonic properties directly from full dark matter merger trees to infer galaxy properties. The package includes code for preprocessing the merger tree, and training the model can be done either as single experiments or as a sweep. Mangrove provides loss functions, learning rate schedulers, models, and a script for doing the training on a GPU.

[ascl:2306.014] AIOLOS: Planetary atmosphere accretion and escape simulations

AIOLOS solves differential equations for hydrodynamics, friction, (thermal) radiation transport and (photo)chemistry for simulating accretion onto, and hydrodynamic escape from, planetary atmospheres. The 1-D multispecies, multiphysics hydrodynamics code, written in C++, compiles in a flexible mode that runs problems with any number of input species, and can be sped up by setting the number of species at compile time, and allows the user to provide initial conditions or boundary conditions if desired. AIOLOS provides output and diagnostic files that give snapshots in time of the state of the simulation. Output files are specific to each species, and diagnostic files contain summary as well as detailed information for, for example, the radiation transport, opacities for all species, and optical cell depths per band, in addition to other information.

[ascl:2306.013] SCONCE-SCMS: Spherical and conic cosmic web finders with extended SCMS algorithms

SCONCE-SCMS detects cosmic web structures, primarily cosmic filaments and the associated cosmic nodes, from a collection of discrete observations with the extended subspace constrained mean shift (SCMS) algorithms on the unit (hyper)sphere (in most cases, the 2D (RA,DEC) celestial sphere), and the directional-linear products space (most commonly, the 3D (RA,DEC,redshift) light cone).

The subspace constrained mean shift (SCMS) algorithm is a gradient ascent typed method dealing with the estimation of local principal curves, more widely known as density ridges. The one-dimensional density ridge traces over the curves where observational data are highly concentrated and thus serves as a natural model for cosmic filaments in our Universe. Modeling cosmic filaments as density ridges enables efficient estimation by the kernel density estimator (KDE) and the subsequent SCMS algorithm in a statistically consistent way. While the standard SCMS algorithm can identify the density ridges in any "flat" Euclidean space, it exhibits large bias in estimating the density ridges on the data space with a non-linear curvature. The extended SCMS algorithms used in SCONCE-SCMS are adaptive to the spherical and conic geometries and resolve the estimation bias of the standard SCMS algorithm on a 2D (RA,DEC) celestial sphere or 3D (RA,DEC,redshift) light cone.

[ascl:2306.012] ZodiPy: Zodiacal emission simulations in timestreams or HEALPix for solar system observers

ZodiPy simulates the zodiacal emission in intensity that an arbitrary solar system observer is predicted to see given an interplanetary dust model, either in the form of timestreams or full-sky HEALPix maps. Written in Python, the code makes zodiacal emission simulations more accessible by providing a simple interface to existing models.

[ascl:2306.011] margarine: Posterior sampling and marginal Bayesian statistics

Margarine computes marginal bayesian statistics given a set of samples from an MCMC or nested sampling run. Specifically, the code calculates marginal Kullback-Leibler divergences and Bayesian dimensionalities using Masked Autoregressive Flows and Kernel Density Estimators to learn and sample posterior distributions of signal subspaces in high dimensional data models, and determines the properties of cosmological subspaces, such as their log-probability densities and how well constrained they are, independent of nuisance parameters. Margarine thus allows for direct and specific comparison of the constraining ability of different experimental approaches, which can in turn lead to improvements in experimental design.

[ascl:2306.010] MOBSE: Massive Objects in Binary Stellar Evolution

MOBSE investigates the demography of merging BHBs. A customized version of the binary stellar evolution code BSE (ascl:1303.014), MOBSE includes metallicity-dependent prescriptions for mass-loss of massive hot stars and upgrades for the evolution of single and binary massive stars.

[ascl:2306.009] Albatross: Stellar stream parameter inference with neural ratio estimation

Albatross analyzes Milky Way stellar streams. This Simulation-Based Inference (SBI) library is built on top of swyft (ascl:2302.016), which implements neural ratio estimation to efficiently access marginal posteriors for all parameters of interest. Using swyft for its internal Truncated Marginal Neural Ratio Estimation (TMNRE) algorithm and sstrax (ascl:2306.008) for fast simulation and modeling, Albatross provides a modular inference pipeline to support parameter inference on all relevant parts of stellar stream models.

[ascl:2306.008] sstrax: Fast stellar stream modelling in JAX

sstrax provides fast simulations of Milky Way stellar stream formation. Using JAX (ascl:2111.002) acceleration to support code compilation, sstrax forward models all aspects of stream formation, including evolution in gravitational potentials, tidal disruption and observational models, in a fully modular way. Although sstrax is a standalone python package, it was also developed to integrate directly with the Albatross (ascl:2306.009) inference pipeline, which performs inference on all relevant aspects of the stream model.

[ascl:2306.007] PhotoParallax: Data-driven photometric parallaxes built with Gaia and 2MASS

PhotoParallax calculates photometric parallaxes for distant stars in the Gaia TGAS catalog without any use of physical stellar models or stellar density models of the Milky Way. It uses the geometric parallaxes to calibrate a photometric model that is purely statistical, which is a model of the data rather than a model of stars per se.

[ascl:2306.006] β-SGP: Scaled Gradient Projection algorithm using β-divergence

β-SGP deconvolves an astronomical image with a known Point Spread Function, providing a means for restoration of telescopic images due to issues ranging from atmospheric turbulence to instrumental aberrations. The code supports improved astrometry, deblending of overlapping sources, faint source detection, and identification of point sources near bright extended objects, and other tasks. β-SGP generalizes the Scaled Gradient Projection (SGP) image deconvolution algorithm using β-divergence as a loss function to restore distorted stellar shapes.

[ascl:2306.005] Delight: Photometric redshift via Gaussian processes with physical kernels

Delight infers photometric redshifts in deep galaxy and quasar surveys. It uses a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift, thus leveraging the advantages of both machine- learning and template-fitting methods by building template SEDs directly from the training data. Delight obtains accurate redshift point estimates and probability distributions and can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts.

[submitted] Turbospectrum_NLTE

Latest version of TS (Turbospectrum), with NLTE capabilities.
Computation of stellar spectra (flux and intensities) in 1D or average stellar atmosphere models.
In order to compute NLTE stellar spectra, additional data is needed, downloadable outside GitHub.
See documentation in DOC folder

Python wrappers are available at https://github.com/EkaterinaSe/TurboSpectrum-Wrapper/ and https://github.com/JGerbs13/TSFitPy
They allow interpolation between models and fitting of spectra to derive stellar parameters.

[ascl:2306.004] TIDYMESS: TIdal DYnamics of Multi-body ExtraSolar Systems

The N-body code TIDYMESS (TIdal DYnamics of Multi-body ExtraSolar Systems) can describe the mass distribution of each body its inertia tensor and directly and self-consistently integrates orbit, spin, and inertia tensors. It manages the deformation of a body follows the tidal Creep model and includes the centrifugal force and tidal force. Written in C++, TIDYMESS is available as a standalone package and also through the AMUSE framework (ascl:1107.007).

[ascl:2306.003] SAVED21cm: Global 21cm signal extraction pipeline

SAVED21cm extracts the 21cm signal from the simulated mock observation for the Radio Experiment for the Analysis of Cosmic Hydrogen (REACH). Though built for the REACH experiment, this 21cm signal extraction pipeline can in principle can be utilized for any global 21cm experiment. The toolkit is based on a pattern recognition framework using the Singular Value Decomposition (SVD) of the 21cm and foreground training set. SAVED21cm finds the patterns in the training sets and properly models the chromatic distortions with a better basis than the polynomials.

[ascl:2306.002] sbi: Simulation-based inference toolkit

Simulation-based inference is the process of finding parameters of a simulator from observations. The PyTorch package sbi performs simulation-based inference by taking a Bayesian approach to return a full posterior distribution over the parameters, conditional on the observations. This posterior can be amortized (i.e. useful for any observation) or focused (i.e.tailored to a particular observation), with different computational trade-offs. The code offers a simple interface for one-line posterior inference.

[ascl:2306.001] HAFFET: Supernovae photometric and spectroscopic data analyzer

HAFFET (Hybrid Analytic Flux FittEr for Transients) analyzes supernovae photometric and spectroscopic data. It handles observational data for a set of targets, estimates their physical parameters, and visualizes the population of inferred parameters. HAFFET defines two classes, snobject for data and fittings for one specific object, and snelist to organize the overall running for a list of objects. The HAFFET package includes utilities for downloading SN data from online sources, intepolating multi band lightcurves, characterizing the first light and rising of SNe with power law fits, and matching epochs of different bands. It can also calculate colors, and/or construct the spectral energy distribution (SED), estimate bolometric LCs and host galaxy extinction, fit the constructed bolometric lightcurves to different models, and identify and fit the absorption minima of spectral lines, in addition to performing other tasks. In addition to utilizing the built-in models, users can add their own models or import models from other python packages.

[ascl:2305.025] CELEBI: Precision localizations and polarimetric data for fast radio bursts

The Australian Square Kilometre Array Pathfinder (ASKAP) has been enabled by the Commensal Real-time ASKAP Fast Transients Collaboration (CRAFT) to detect Fast Radio Bursts (FRBs) in real-time and save raw antenna voltages containing FRB detections. CELEBI, the CRAFT Effortless Localization and Enhanced Burst Inspection pipeline, extends CRAFT’s existing software to process ASKAP voltages to produce sub-arcsecond precision localizations and polarimetric data at time resolutions as fine as 3 ns of FRB events. CELEBI uses Nextflow (ascl:2305.024) to link together Bash and Python code to perform software correlation, interferometric imaging, and beamforming, thereby making use of common astronomical software packages.

[ascl:2305.024] Nextflow: DSL for data-driven computational pipelines

Nextflow enables scalable and reproducible scientific workflows using software containers. It allows the adaptation of pipelines written in the most common scripting languages. Its fluent DSL simplifies the implementation and the deployment of complex parallel and reactive workflows on clouds and clusters. Nextflow supports deploying workflows on a variety of execution platforms including local, HPC schedulers, AWS Batch, Google Cloud Life Sciences, and Kubernetes. Additionally, it provides support for workflow dependencies through built-in support for, for example, Conda, Spack, Docker, Podman, Singularity, and Modules.

[ascl:2305.023] GLASS: Cosmological simulations on the sphere

GLASS (Generator for Large Scale Structure) produces cosmological simulations on the sphere. The full, three-dimensional past light cone of the observer is discretized into a sequence of nested shells, which are further discretized in the angular dimensions into maps of the sphere. GLASS was originally designed to simulate cosmic matter, weak gravitational lensing, and galaxy positions, but its flexible design and open architecture allows it to be used for a wide range of cosmological and astrophysical simulations on the sphere.

[ascl:2305.022] GrGadget: Evolve metric perturbations in the weak field limit

GrGadget merges the Particle-Mesh (PM) relativistic GEVOLUTION code (ascl:1608.014) with the TreePM GADGET-4 code (ascl:2204.014) to create a TreePM simulation code that represents metric perturbations at the scales where they are relevant while resolving non-linear structures. The better resolution of the highly non-linear regime improves the representation of the relativistic fields sampled on the mesh with respect to PM-only simulations.

[ascl:2305.021] COLIBRI: Cosmological libraries in Python

COLIBRÌ (which roughly stands for “Cosmological Libraries”) computes cosmological quantities such as ages, distances, power spectra, and correlation functions. It supports Lambda-CDM cosmologies plus extensions including massive neutrinos, non-flat geometries, evolving dark energy (w0-wa) models, and numerical recipes for f(R) gravity. COLIBRÌ is built especially for large-scale structure purposes and can interact with the Boltzmann solvers CAMB (ascl:1102.026) and CLASS (ascl:1106.020).

[ascl:2305.020] JEDI: James's EVE Dimming Index

JEDI searches for and characterizes coronal dimming in light curves produced from the Solar Dynamics Observatory (SDO) Extreme Ultraviolet (EUV) Variability Experiment (EVE). The suite has a wrapper script that calls other functions, which can also be run independently assuming needed inputs from prior functions are provided. JEDI's functions fit light curves and return the best fit, compute precision for iron light curves, and find the biggest dimming depth and its time in a given light curve. JEDI also includes functions for finding the duration of the dimming, minimum, maximum, and mean slope of dimming of a light curve, and for identifying the biggest peak in two light curves, time shifting them so the peaks are concurrent, scaling them so the peaks are the same magnitude, and then subtracting them, among other useful functions.

[ascl:2305.019] sterile-dm: Sterile neutrino production

The sterile neutrino production code sterile-dm incorporates new elements to the calculations of the neutrino opacity at temperatures 10 MeV ≤ T ≤ 10 GeV and folds the asymmetry redistribution and opacity calculations into the sterile neutrino production computation, providing updated PSDs for the range of parameters relevant to the X-ray excess. The code requires several data files, which are included. With each run, sterile-dm creates a new output sub-directory that contains a parameter file listing the mass, mixing angle, initial lepton asymmetry and other information, a state file, which includes, among other states, the temperature and FRW coordinate time, and a set of snapshot files, one for each line in the state file.

[ascl:2305.018] GWSurrogate: Gravitational wave surrogate models

GWSurrogate provides an easy to use interface to gravitational wave surrogate models. Surrogates provide a fast and accurate evaluation mechanism for gravitational waveforms which would otherwise be found through solving differential equations. These equations must be solved in the “building” phase, which was performed using other codes.

[ascl:2305.017] simple-m2m: Extensions to the standard M2M algorithm for full modeling of observational data

Made-to-measure (M2M) is a standard technique for modeling the dynamics of astrophysical systems in which the system is modeled with a set of N particles with weights that are slowly optimized to fit a set of constraints while integrating these particles forward in the gravitational potential. Simple-m2m extends this standard technique to allow parameters of the system other than the particle weights to be fit as well, including nuisance parameters that describe the observer's relation to the dynamical system (e.g., the inclination) or parameters describing an external potential.

[ascl:2305.016] gw_pta_emulator: Gravitational Waves via Pulsar Timing Arrays

The gw_pta_emulator reads in gravitational wave (GW) characteristic strain spectra from black-hole population simulations, re-bins for the user's observing baseline, and constructs new spectra. The user can train a Gaussian process to emulate the spectral behavior at all frequencies across the astrophysical parameter space of supermassive black-hole binary environments.

[ascl:2305.015] EIDOS: Modeling primary beams of radio astronomy antennas

EIDOS models the primary beam of radio astronomy antennas. The code can be used to create MeerKAT L-band beams from both holographic (AH) observations and EM simulations within a maximum diameter of 10 degrees. The beam model is less accurate at higher frequencies, and performs much better below 1400 MHz. The diagonal terms of the model beam Jones matrix are much better known than the off-diagonal terms. The performance of EIDOS is dependent on the quality of the given AH and EM datasets; as more accurate AH models and EM simulations become available, this pipeline can be used to create more accurate sparse representation of primary beams using Zernike polynomials.

[ascl:2305.014] DP3: Streaming processing pipeline for radio interferometric data

DP3 (the Default Preprocessing Pipeline) is the LOFAR data pipeline processing program and is the successor to DPPP (ascl:1804.003). It performs many kinds of operations on the data in a pipelined way so the data are read and written only once. DP3 preprocesses the data of a LOFAR observation by executing steps such as flagging or averaging. Such steps can be used for the raw data as well as the calibrated data by defining the data column to use. One or more of the following steps can be defined as a pipeline. DP3 has an implicit input and output step. It is also possible to have intermediate output steps. DP3 comes with predefined steps, but also allows the user to plug in arbitrary steps implemented in either C++ or Python.

[ascl:2305.013] aartfaac2ms: Aartfaac datasets converter

aartfaac2ms converts raw Aartfaac correlator files to the casacore (ascl:1912.002) measurement set format. It phase rotates the data to a common phase center, and (optionally) flags, averages, and compresses the data. The code includes a tool, afedit, to splice a raw Aartfaac set based on LST.

[ascl:2305.012] KERN: Radio telescope toolkit

KERN contains most of the standard tools needed to work with radio telescope data. The suite saves time and reduces frustration in setting up of scientific pipelines, and also improves scientific reproducibility. It includes a wide variety of packages, including 21cmfast (ascl:1102.023), BRATS (ascl:1806.025), CARTA (ascl:2103.031), casacore (ascl:1912.002), CubiCal (ascl:1805.031), DDFacet (ascl:2305.008), PyBDSF (ascl:1502.007),TiRiFiC (ascl:1208.008), WSClean (ascl:1408.023), and many others. KERN can be run on a supported platform such as Ubuntu, with Docker and Singularity, or in a virtual machine.

[ascl:2305.011] DarkMappy: Mapping the dark universe

DarkMappy reconstructs maximum a posteriori (MAP) convergence maps by formulating an unconstrained Bayesian inference problem in order to implement hybrid Bayesian dark-matter reconstruction techniques on the plane and on the celestial sphere. These convergence maps support principled uncertainty quantification and provide hypothesis testing of structure, from which it is possible to distinguish between physical objects and artifacts of the reconstruction.

[ascl:2305.010] FLAGLET: Fast and exact wavelet transform on the ball

FLAGLET computes flaglet transforms with arbitrary spin direction, probing the angular features of this generic wavelet transform for rapid analysis of signals from wavelet coefficients. The code enables the decomposition of a band-limited signal into a set of flaglet maps that capture all information contained in the initial band-limited map, and it can reconstruct the individual flaglets at varying resolutions. FLAGLET relies upon the SSHT (ascl:2207.034), S2LET (ascl:1211.001), and SO3 codes to provide angular transforms and sampling theorems, as well as the FFTW (ascl:1201.015) code to compute Fourier transforms.

[ascl:2305.009] breizorro: Image masking tool

Given a FITS image, breizorro creates a binary mask. The software allows the user control various parameters and functions, such as setting a sigma threshold for masking, merging in or subtracting one or more masks or region files, filling holes, applying dilation within a defined radius of pixels, and inverting the mask.

[ascl:2305.008] DDFacet: Facet-based radio imaging package

DDFacet provides a wideband wide-field spectral imaging and deconvolution framework that accounts for generic direction-dependent effects (DDEs). It implements a wide-field coplanar faceting scheme and uses nontrivial facet-dependent w-kernels to correct for noncoplanarity within the facets. In the imaging and deconvolution steps, DDFacet can handle generic, spatially discrete, time-frequency-baseline-direction-dependent full polarization Jones matrices, and computes a direction dependent PSF for use in the minor cycle of deconvolution for time-frequency-baseline dependent Mueller matrices. The code also allows for the effects of time and bandwidth averaging to be explicitly incorporated into deconvolution. DDFacet has been successfully tested with data diverse telescopes such as LOFAR, VLA, MeerKAT AR1, and ATCA.

[ascl:2305.007] Stimela: Containerized radio interferometry scripting framework

stimela provides a system-agnostic scripting framework for simulating, processing, and imaging radio interferometric data. The framework executes radio interferometry related tasks such as imaging, calibration, and data synthesis in Docker containers using Python modules. stimela offers a simple interface to packages that perform these tasks rather than doing any data processing, synthesis or analysis itself. stimela only requires Docker and Python. Moreover, because of Docker, a stimela script runs the same way (in the same iso­lated environment) regardless of the host machine’s settings, thus providing a user-friendly and modular scripting environment that gives general users easy access to novel radio interferometry calibration, imaging, and synthesis packages.

[ascl:2305.006] QuartiCal: Fast radio interferometric calibration

QuartiCal is the successor to CubiCal (ascl:1805.031). It implements a suite of fast radio interferometric calibration routines exploiting complex optimization. Unlike CubiCal, QuartiCal allows for any available Jones terms to be combined. It can also be deployed on a cluster.

[ascl:2305.005] killMS: Direction-dependent radio interferometric calibration package

killMS implements two very efficient algorithms for solving the Direction-Dependent calibration problem (also known as third generation calibration). This problem naturally arises in the Radio Interferometry Measurement Equation (RIME), but only became overwhelmingly problematic with the construction of the SKA precursors and pathfinders. Solving for the DDE calibration problem basically consists in inverting a number of non-linear equations, while the system is very large and often subject to ill conditioning. The two algorithms killMS uses are based on complex optimization techniques and exploit algorithmic shortcuts; killMS also runs an extended Kalman filter.

[ascl:2305.004] katdal: MeerKAT Data Access Library

katdal interacts with the chunk stores and HDF5 files produced by the MeerKAT radio telescope and its predecessors (KAT-7 and Fringe Finder), which are collectively known as MeerKAT Visibility Format (MVF) data sets. The library uses memory carefully, allowing data sets to be inspected and partially loaded into memory. Data sets may be concatenated and split via a flexible selection mechanism. In addition, katdal provides a script to convert these data sets to CASA MeasurementSets.

[ascl:2305.003] extrapops: Fast simulation and analysis of extra-galactic binary GW sources

extrapops simulates extra-galactic populations of gravitational waves sources and models their emission during the inspiral phase. The code approximately assesses the detectability of individual sources by LISA and computes the background due to unresolved sources in the LISA band using different methods. The simulated populations can be saved in a format compatible with LISA LDC. Simulations are well calibrated to produce accurate background calculations and fair random generation at the tails of the distributions, which is important for accurate probability of detectable events. extrapops uses a number of ad-hoc techniques for rapid simulation and allows room for further optimization up to almost 1 order of magnitude.

[ascl:2305.002] Virtual Telescope: Next-Generation Space Telescope Simulator

Virtual Telescope predicts the signal-to-noise and other parameters of imaging and/or spectroscopic observations as a function of telescope size, detector noise, and other factors for the Next-Generation Space Telescope.

[ascl:2305.001] FRIDDA: Fisher foRecast code for combIned reDshift Drift and Alpha

FRIDDA forecasts the cosmological impact of measurements of the redshift drift and the fine-structure constant (alpha) as well as their combination. The code is based on Fisher Matrix Analysis techniques and works for various fiducial cosmological models. Though designed for the ArmazoNes high Dispersion Echelle Spectrograph (ANDES), it is easily adaptable to other fiducial cosmological models and to other instruments with similar scientific goals.

[ascl:2304.006] JET: JWST Exoplanet Targeting

JET (JWST Exoplanet Targeting) optimizes lists of exoplanet targets for atmospheric characterization by the James Webb Space Telescope (JWST). The software uses catalogs of planet detections, either simulated, or actual and categorizes targets by radius and equilibrium temperature; it also estimates planet masses and generates model spectra and simulated instrument spectra. JET then performs a statistical analysis to determine if the instrument spectra can confirm an atmospheric detection and finally ranks the targets within each category by observation time required for detection.

[ascl:2304.005] FALCO: Fast Linearized Coronagraph Optimizer in Python

FALCO (Fast Linearized Coronagraph Optimizer) performs coronagraphic focal plane wavefront correction. It includes routines for pair-wise probing estimation of the complex electric field and Electric Field Conjugation (EFC) control. FALCO utilizes and builds upon PROPER (ascl:1405.006) and rapidly computes the linearized response matrix for each DM, which facilitates re-linearization after each control step for faster DM-integrated coronagraph design and wavefront correction experiments. A MATLAB implementation of FALCO (ascl:2304.004) is also available.

[ascl:2304.004] FALCO: Fast Linearized Coronagraph Optimizer in MATLAB

FALCO (Fast Linearized Coronagraph Optimizer) performs coronagraphic focal plane wavefront correction. It includes routines for pair-wise probing estimation of the complex electric field and Electric Field Conjugation (EFC) control. FALCO utilizes and builds upon PROPER (ascl:1405.006) and rapidly computes the linearized response matrix for each DM, which facilitates re-linearization after each control step for faster DM-integrated coronagraph design and wavefront correction experiments. A Python 3 implementation of FALCO (ascl:2304.005) is also available.

[ascl:2304.003] BatAnalysis: HEASOFT wrapper for processing Swift-BAT data

BatAnalysis processes and analyzes Swift Burst Alert Telescope (BAT) survey data in a comprehensive computational pipeline. The code downloads BAT survey data, batch processes the survey observations, and extracts light curves and spectra for each survey observation for a given source. BatAnalysis allows for the use of BAT survey data in advanced analyses of astrophysical sources including pulsars, pulsar wind nebula, active galactic nuclei, and other known/unknown transient events that may be detected in the hard X-ray band. BatAnalysis can also create mosaicked images at different time bins and extract light curves and spectra from the mosaicked images for a given source.

[ascl:2304.002] Applefy: Robust detection limits for high-contrast imaging

Applefy calculates detection limits for exoplanet high contrast imaging (HCI) datasets. The package provides features and functionalities to improve the accuracy and robustness of contrast curve calculations. Applefy implements the classical approach based on the t-test, as well as the parametric boostrap test for non-Gaussian residual noise. Applefy enables the comparison of imaging results across instruments with different noise characteristics.

[ascl:2304.001] ASSIST: Solar system test particles trajectories integrator

ASSIST integrates test particle trajectories in the field of the Sun, Moon, planets, and massive asteroids, with the positions of the masses obtained from the JPL DE441 ephemeris and its associated asteroid perturber file. Using REBOUND's (ascl:1110.016) IAS15 integrator, ASSIST incorporates the most significant gravitational harmonics and general relativistic corrections and accounts for position- and velocity-dependent non-gravitational effects. The first-order variational equations are included for all terms to support orbit fitting and covariance mapping.

[ascl:2303.020] HaloGraphNet: Predict halo masses from simulations

HaloGraphNet predicts halo masses from simulations using Graph Neural Networks. Given a dark matter halo and its galaxies, this software creates a graph with information about the 3D position, stellar mass and other properties. It then trains a Graph Neural Network to predict the mass of the host halo. Data are taken from the CAMELS hydrodynamic simulations.

[ascl:2303.019] pulsar_spectra: Pulsar flux density measurements, spectral models fitting, and catalog

pulsar_spectra provides a pulsar flux density catalog and automated spectral fitting software for finding spectral models. The package can also produce publication-quality plots and allows users to add new spectral measurements to the catalog. The spectral fitting software uses robust statistical methods to determine the best-fitting model for individual pulsar spectra.

[ascl:2303.018] MORPHOFIT: Morphological analysis of galaxies

MORPHOFIT consists of a series of modules for estimating galaxy structural parameters. The package uses SEXTRACTOR (ascl:1010.064) in forced photometry mode to get an initial estimate of the galaxy structural parameters and create a multiband catalog. It also uses GALFIT (ascl:1010.064), running it on galaxy stamps and galaxy regions from the parent image and also on galaxies from the full image using SEXTRACTOR properties as input. MORPHOFIT has been optimized and tested in both low-density and crowded environments, and can recover the input structural parameters of galaxies with good accuracy.

[ascl:2303.017] bajes: Bayesian Jenaer software

bajes [baɪɛs] provides a user-friendly interface for setting up a Bayesian analysis for an arbitrary model, and is specialized for the analysis of gravitational-wave and multi-messenger transients. The code runs a parameter estimation job, inferring the properties of the input model. bajes is designed to be simple-to-use and light-weighted with minimal dependencies on external libraries. The user can set up a pipeline for parameters estimation of multi-messenger transients by writing a configuration file containing the information to be passed to the executables. The package also includes tools and methods for data analysis of multi-messenger signals. The pipeline incorporates an interface with reduced-order-quadratude (ROQ) interpolants. In particular, the ROQ pipeline relies on the output provided by PyROQ-refactored.

[ascl:2303.016] SatGen: Semi-analytical satellite galaxy and dark matter halo generator

SatGen generates satellite-galaxy populations for host halos of desired mass and redshift. It combines halo merger trees, empirical relations for galaxy-halo connection, and analytic prescriptions for tidal effects, dynamical friction, and ram-pressure stripping. It emulates zoom-in cosmological hydrosimulations in certain ways and outperforms simulations regarding statistical power and numerical resolution.

[ascl:2303.015] SIDM: Density profiles of self-interacting dark-matter halos with inhabitant galaxies

The SIDM model combines the isothermal Jeans model and the model of adiabatic halo contraction into a simple semi-analytic procedure for computing the density profile of self-interacting dark-matter (SIDM) haloes with the gravitational influence from the inhabitant galaxies. It agrees well with cosmological SIDM simulations over the entire core-forming stage and up to the onset of gravothermal core-collapse. The fast speed of the method facilitates analyses that would be challenging for numerical simulations.

[ascl:2303.014] Delphes: Fast simulation of a generic collider experiment

Delphes simulates a fast multipurpose detector response. The simulation includes a tracking system, embedded into a magnetic field, calorimeters and a muon system. The Delphes framework is interfaced to standard file formats (e.g. Les Houches Event File or HepMC) and outputs observables such as isolated leptons, missing transverse energy and collection of jets that can be used for dedicated analyses. The simulation of the detector response takes into account the effect of magnetic field, the granularity of the calorimeters and sub-detector resolutions. Visualization of the final state particles is also built-in using the corresponding ROOT library.

[ascl:2303.013] FastJet: Jet finding in pp and e+e− collisions

The FastJet package provides fast native implementations of many sequential recombination algorithms, including the longitudinally invariant kt longitudinally invariant inclusive Cambridge/Aachen and anti-kt jet finders. It also provides a uniform interface to external jet finders via a plugin mechanism. FastJet also includes tools for calculating jet areas and performing background (pileup/UE) subtraction and for jet substructure analyses.

[ascl:2303.012] EvoEMD: Cosmic Evolution with an Early Matter-Dominated era

EvoEMD evaluates cosmic evolution with or without an early matter dominated (EMD) era. The framework includes global parameter, particle, and process systems, and different methods for Hubble parameter calculation. EvoEMD automatically builds up the Boltzmann equation according to the user's definition of particle and process,solves the Boltzmann equation using 4th order Runge-Kutta method with adaptive steps tailored to cosmology application, and caches the collision rate calculation results for fast evaluation.

[ascl:2303.011] Scri: Manipulate time-dependent functions of spin-weighted spherical harmonics

Scri manipulates time-dependent functions of spin-weighted spherical harmonics. It implements the BMS transformations of the most common gravitational waveforms, including the Newman-Penrose quantity ψ4, the Bondi news function, the shear spin coefficient σ, and the transverse-traceless metric perturbation h, as well as the remaining Newman-Penrose quantities ψ0 through ψ3.

[ascl:2303.010] spinsfast: Fast and exact spin-s spherical harmonic transforms

spinsfast is a fast spin-s spherical harmonic transform algorithm, which is flexible and exact for band-limited functions. It permits the computation of several distinct spin transforms simultaneously. Specifically, only one set of special functions is computed for transforms of quantities with any spin, namely the Wigner d matrices evaluated at π/2, which may be computed with efficient recursions. For any spin, the computation scales as O(L^3), where L is the band limit of the function.

[ascl:2303.009] Pandora: Fast exomoon transit detection algorithm

Pandora searches for exomoons by employing an analytical photodynamical model that includes stellar limb darkening, full and partial planet-moon eclipses, and barycentric motion of planet and moon. The code can be used with nested samplers such as UltraNest (ascl:1611.001) or dynesty (ascl:1809.013). Pandora is fast, calculating 10,000 models and log-likelihood evaluation per second (give or take an order of magnitude, depending on parameters and data); this means that a retrieval with 250 Mio. evaluations until convergence takes about 5 hours on a single core. For searches in large amounts of data, it is most efficient to assign one core per light curve.

[ascl:2303.008] nd-redshift: Number Density Redshift Evolution Code

Comparing galaxies across redshifts via cumulative number densities is a popular way to estimate the evolution of specific galaxy populations. nd-redshift uses abundance matching in the ΛCDM paradigm to estimate the median change in number density with redshift. It also provides estimates for the 1σ range of number densities corresponding to galaxy progenitors and descendants.

[ascl:2303.007] PyCom: Interstellar communication

PyCom provides function calls for deriving the optimal communication scheme to maximize the data rate between a remote probe and home-base. It includes models for the loss of photons from diffraction, technological limitations, interstellar extinction and atmospheric transmission, and manages major atmospheric, zodiacal, stellar and instrumental noise sources. It also includes scripts for creating figures appearing in the referenced paper.

[ascl:2303.006] GPCC: Gaussian process cross-correlation for time delay estimation

Gaussian Process Cross-Correlation (GPCC) uses Gaussian processes to estimate time delays for reverberation mapping (RM) of Active Galactic Nuclei (AGN). This statistically principled model delivers a posterior distribution for the delay and accounts for observational noise and the non-uniform sampling of the light curves. Written in Julia, GPCC quantifies the uncertainty and propagates it to subsequent calculations of dependent physical quantities, such as black hole masses. The code delivers out-of-sample predictions, which enables model selection, and can calculate the joint posterior delay for more than two light curves. Though written for RM, the software can also be applied to other fields where cross-correlation analysis is performed.

[ascl:2303.005] Blobby3D: Bayesian inference for gas kinematics

Blobby3D performs Bayesian inference for gas kinematics on emission line observations of galaxies using Integral Field Spectroscopy. The code robustly infers gas kinematics for regularly rotating galaxies even if the gas profiles have significant substructure. Blobby3D also infers gas kinematic properties free from the effects of beam smearing (where beam smearing is the effect of the observational seeing spatially blurring the gas profiles), which has significant effects on the observed gas kinematic properties, particularly the observed velocity dispersion.

[ascl:2303.004] naif: Frequency analysis package

naif extracts frequencies and respective amplitudes from time-series, such as that of an orbital coordinate. Based on the Numerical Analysis of Fundamental Frequencies (NAFF) algorithm and written in Python, naif offers some improvements, particularly in computation time. It also offers functions to plot the power-spectrum before extraction of each frequency, which can be useful for debugging particular orbits.

[ascl:2303.003] SeeKAT: Localizer for transients detected in tied-array beams

SeeKAT is a Python implementation of a novel maximum-likelihood estimation approach to localizing transients and pulsars detected in multiple MeerKAT tied-array beams at once to (sub-)arcsecond precision. It reads in list of detections (RA, Dec, S/N) and the beam PSF and computes a covariance matrix of the S/N value ratios, assuming 1-sigma Gaussian errors on each measurement. It models the aggregate beam response by arranging beam PSFs appropriately relative to each other and calculates a likelihood distribution of obtaining the observed S/N in each beam according to the modeled response. In addition, SeeKAT can plot the likelihood function over RA and Dec with 1-sigma uncertainty, overlaid on the beam coordinates and sizes.

[ascl:2303.002] line_selections: Automatic line detection for large spectroscopic surveys

The Python code line_selections reads synthetic "full" spectra and elemental spectra, automatically identifies the detectable lines at a given resolution (provided the linelist used to compute the spectra), and returns a table containing various properties of the lines (e.g., purity, central wavelength, and depth). The code then stores the information in a pandas DataFrame. line_selections demonstrates where chemical information is present in a stellar spectrum, and allows the user to optimize observational strategies, such as choosing resolution and spectra windows, as well as analysis codes with the application of high-quality masks.

[submitted] World Observatory

World Observatory visualizes S/N-versus-cost tradeoffs for large optical and near-infrared telescopes. Both mid-latitude and Arctic/Antarctic sites can be considered; the intent is a simple simulation to grow intuition for where major capital costs lie relative to key observatory design choices, and against expected scientific performance at various sites. User-defined unit costs for (a possibly "effective") roadway, enclosure, aperture, focal length, and adaptive optics can be scaled up for polar sites, and down for better seeing and lower sky brightness in K-band. Observatory models and results are immediately displayed side-by-side. Either point-source-detection S/N or recovery of bulge-to-total ratios in a simulated galaxy survey are divided by the total project cost, thus providing a universal metric.

[ascl:2303.001] cysgp4: Wrapper for C++ SGP4 satellite library

The cysgp4 Cython-powered package wraps the C++ SGP4 Library for computing satellite positions from two-line elements (TLE). It provides similar functionality as the sgp4 Python package, though also works well with arrays of TLEs and/or observing times and makes use of multi-core platforms (via OpenMP) to improve processing times.

[ascl:2302.026] HDMSpectra: Dark Matter Spectra from the electroweak to the Planck scale

HDMSpectra computes the decay spectrum for dark matter with masses above the scale of electroweak symmetry breaking, down to Planck scale and including all relevant electroweak interactions. The code determines the distribution of stable states for photons, neutrinos, positrons, and antiprotons.

[ascl:2302.025] Diffmah: Differentiable models of halo and galaxy formation history

Diffmah approximates the growth of individual halos as a simple power-law function of time, where the power-law index smoothly decreases as the halo transitions from the fast-accretion regime at early times to the slow-accretion regime at late times. The code has a typical accuracy of 0.1 dex for times greater than one billion years in halos of mass greater than 10e11 M_sun. Diffmah self-consistently captures the mean and variance of halo mass accretion rates across long time scales, and it generates Monte Carlo simulations of cosmologically-representative and differentiable halo histories.

[ascl:2302.024] DSPS: Differentiable Stellar Population Synthesis

DSPS synthesizes stellar populations, leading to fully-differentiable predictions for galaxy photometry and spectroscopy. The code implements an empirical model for stellar metallicity, and it also supports the Diffstar (ascl:2302.012) model of star formation and dark matter halo history. DSPS rapidly generates and simulates galaxy-halo histories on both CPU and GPU hardware.

[ascl:2302.023] AART: Adaptive Analytical Ray Tracing

AART (Adaptive Analytical Ray Tracing) exploits the integrability properties of the Kerr spacetime to compute high-resolution black hole images and their visibility amplitude on long interferometric baselines. It implements a non-uniform adaptive grid on the image plane suitable to study black hole photon rings (narrow ring-shaped features, predicted by general relativity but not yet observed). The code implements all the relevant equations required to compute the appearance of equatorial sources on the (far) observer's screen.

[ascl:2302.022] RALF: RADEX Line Fitter

The RADEX Line Fitter provides a Python 3 interface that calls RADEX (ascl:1010.075) to make a non-LTE fit to a set of observed lines and derive the column density of the molecule that produced the lines and optionally also the molecular hydrogen (H2) number density or the kinetic temperature of the molecule. This code requires RADEX to be installed locally.

[ascl:2302.021] AMICAL: Aperture Masking Interferometry Calibration and Analysis Library

AMICAL (Aperture Masking Interferometry Calibration and Analysis Library) processes Aperture Masking Interferometry (AMI) data from major existing facilities, such as NIRISS on the JWST, SPHERE and VISIR from the European Very Large Telescope (VLT) and VAMPIRES from SUBARU telescope. The library cleans the reduced datacube from the standard instrument pipelines, extracts the interferometrical quantities (visibilities and closure phases) using a Fourier sampling approach, and calibrates those quantities to remove the instrumental biases. In addition, two external packages (CANDID and Pymask) are included to analyze the final outputs obtained from a binary-like sources (star-star or star-planet); these stand-alone packages are interfaced with AMICAL to quickly estimate scientific results (e.g., separation, position angle, contrast ratio, and contrast limits) using different approaches.

[ascl:2302.020] UBER: Universal Boltzmann Equation Solver

UBER (Universal Boltzmann Equation Solver) solves the general form of Fokker-Planck equation and Boltzmann equation, diffusive or non-diffusive, that appear in modeling planetary radiation belts. Users can freely specify the coordinate system, boundary geometry and boundary conditions, and the equation terms and coefficients. The solver works for problems in one to three spatial dimensions. The solver is based upon the mathematical theory of stochastic differential equations. By its nature, the solver scheme is intrinsically Monte Carlo, and the solutions thus contain stochastic uncertainty, though the user may dictate an arbitrarily small relative tolerance of the stochastic uncertainty at the cost of longer Monte Carlo iterations.

[ascl:2302.019] MADCUBA: MAdrid Data CUBe Analysis

MADCUBA analyzes astronomical datacubes and multiple spectra from various astronomical facilities, including ALMA, Herschel, VLA, IRAM 30m, APEX, GBT, and others. These telescopes, and in particular ALMA, generate extremely large datacubes (spatial, spectral and polarization). This software combines a user-friendly interface and powerful data analysis system to derive the physical conditions of molecular gas, its chemical complexity and the kinematics from datacubes. Built using the ImageJ (ascl:1206.013) infrastructure, MADCUBA visualizes astronomical datacubes with thousands on spectral channels, and datasets with thousands of spectra; it also identifies molecular species using publicly available molecular catalogs. It can automatically derive the physical parameters of the molecular species: column density, excitation temperature, velocity and linewidths and provides the best non-linear least-squared fit using the Levenberg-Marquardt algorithm, among other tasks.

[ascl:2302.018] GCP: Automated GILDAS-CLASS Pipeline

This library of scripts provides a simple interface for running the CLASS software from GILDAS (ascl:1305.010) in a semi-automatic way. Using these scripts, one can extract and organize spectra from data files in CLASS format (for example, .30m and .40m), reduce them, and even combine or average them once they are reduced. The library contains five Python scripts and two optional Julia scripts.

[ascl:2302.017] RichValues: Managing numeric values with uncertainties and upper/lower limits

RichValues transforms numeric values with uncertainties and upper/lower limits to create "rich values" that can be written in plain text documents in an easily readable format and used to propagate uncertainties automatically. Rich values can also be exported in the same formatting style as the import. The RichValues library uses a specific formatting style to represent the different kinds of rich values with plain text; it can also be used to create rich values within a script. Individual rich values can be used in, for example, tuples, lists, and dictionaries, and also in arrays and tables.

[ascl:2302.016] swyft: Scientific simulation-based inference at scale

swyft implements Truncated Marginal Neural Radio Estimation (TMNRE), a Bayesian parameter inference technique for complex simulation data. The code improves performance by estimating low-dimensional marginal posteriors rather than the joint posteriors of distributions, while also targeting simulations to targets of observational interest via an indicator function. The use of local amortization permits statistical checks, enabling validation of parameters that cannot be performed using sampling-based methods. swyft is also based on stochastic simulations, mapping parameters to observational data, and incorporates a simulator manager.

[ascl:2302.015] FCFC: C toolkit for computing correlation functions from pair counts

FCFC (Fast Correlation Function Calculator) computes correlation functions from pair counts. It supports the isotropic 2-point correlation function, anisotropic 2PCF, 2-D 2PCF, and 2PCF Legendre multipoles, among others. Written in C, FCFC takes advantage of three parallelisms that can be used simultaneously, distributed-memory processes via Message Passing Interface (MPI), shared-memory threads via Open Multi-Processing (OpenMP), and single instruction, multiple data (SIMD).

[ascl:2302.014] kima: Exoplanet detection in RVs with DNest4 and GPs

kima fits Keplerian curves to a set of RV measurements, using the Diffusive Nested Sampling (ascl:1010.029) algorithm to sample the posterior distribution for the model parameters. Additionally, the code can calculate the fully marginalized likelihood of a model with a given number of Keplerians and also infer the number of Keplerian signals detected in a given dataset. kima implements dedicated models for different analyses of a given dataset. The models share a common organization, but each has its own parameters (and thus priors) and settings.

[ascl:2302.013] SASHIMI-C: Semi-Analytical SubHalo Inference ModelIng for Cold Dark Matter

SASHIMI-C calculates various subhalo properties efficiently using semi-analytical models for cold dark matter (CDM), providing a full catalog of dark matter subhalos in a host halo with arbitrary mass and redshift. Each subhalo is characterized by its mass and density profile both at accretion and at the redshift of interest, accretion redshift, and effective number (or weight) corresponding to that particular subhalo. SASHIMI-C computes the subhalo mass function without making any assumptions such as power-law functional forms; the only assumed power law is that for the primordial power spectrum predicted by inflation. The code is not limited to numerical resolution nor to Poisson shot noise, and its results are well in agreement with those from numerical N-body simulations.

[ascl:2302.012] Diffstar: Differentiable star formation histories

Diffstar fits the star formation history (SFH) of galaxies to a smooth parametric model. Diffstar differs from existing SFH models because the parameterization of the model is directly based on basic features of galaxy formation physics, including halo mass assembly history, accretion of gas into the dark matter halo, the fraction of gas that is converted into stars, the time scale over which star formation occurs, and the possibility of rejuvenated star formation. The SFHs of a large number of simulated galaxies can be fit in parallel using mpi4py.

[ascl:2302.011] UniverseMachine: Empirical model for galaxy formation

The UniverseMachine applies simple empirical models of galaxy formation to dark matter halo merger trees. For each model, it generates an entire mock universe, which it then observes in the same way as the real Universe to calculate a likelihood function. It includes an advanced MCMC algorithm to explore the allowed parameter space of empirical models that are consistent with observations.

[ascl:2302.010] SASHIMI-W: Semi-Analytical SubHalo Inference ModelIng for Warm Dark Matter

SASHIMI-W calculates various subhalo properties efficiently using semi-analytical models for warm dark matter (WDM); the code is based on the extended Press-Schechter formalism and subhalos' tidal evolution prescription. The calculated constraints are independent of physics of galaxy formation and free from numerical resolution and the Poisson noise, and its results are well in agreement with those from numerical N-body simulations.

[ascl:2302.009] EXOTIC: EXOplanet Transit Interpretation Code

EXOTIC (EXOplanet Transit Interpretation Code) analyzes photometric data of transiting exoplanets into lightcurves and retrieves transit epochs and planetary radii. The software reduces images of a transiting exoplanet into a lightcurve, and fits a model to the data to extract planetary information crucial to increasing the efficiency of larger observational platforms. EXOTIC is written in Python and supports the citizen science project Exoplanet Watch. The software runs on Windows, Macintosh, and Linux/Unix computer, and can also be used via Google Colab.

[ascl:2302.008] HawkingNet: Finding Hawking points in the Cosmic Microwave Background

HawkingNet searches for Hawking points in large Cosmic Microwave Background (CMB) data sets. It is based on the deep residual network ResNet18 and consists of eighteen neural layers. Written in Paython, HawkingNet inputs the CMB data, processes the data through its internal network trained for data classification, and outputs the result in a form of a classification score that indicates how confident it is that a Hawking point is contained in the image patch.

[ascl:2302.007] AnalyticLC: Dynamical modeling of planetary systems

AnalyticLC generates an analytic light-curve, and optionally RV and astrometry data, from a set of initial (free) orbital elements and simultaneously fits these data. Written in MATLAB, the code is fast and efficient, and provides insight into the motion of the orbital elements, which is difficult to obtain from numerical integration. A Python wrapper for AnalyticLC is available separately.

[ascl:2302.006] RCR: Robust Chauvenet Outlier Rejection

RCR provides advanced outlier rejection that is easy to use. Both sigma clipping, the simplest form of outlier rejection, and traditional Chauvenet rejection make use of non-robust quantities, the mean and standard deviation, which are sensitive to the outliers that they are being used to reject. This limits such techniques to samples with small contaminants or small contamination fractions. RCR instead first makes use of robust replacements for the mean, such as the median and the half-sample mode, and similar robust replacements for the standard deviation. RCR has been carefully calibrated and can be applied to samples with both large contaminants and large contaminant fractions (sometimes in excess of 90% contaminated).

[ascl:2302.005] celmech: Sandbox for celestial mechanics calculations

celmech provides a variety of analytical and semianalytical tools for celestial mechanics and dynamical astronomy. The package interfaces closely with the REBOUND N-body integrator (ascl:1110.016), thus facilitating comparisons between calculation results and direct N-body integrations. celmech can isolate the contribution of particular resonances to a system's dynamical evolution, and can develop simple analytical models with the minimum number of terms required to capture a particular dynamical phenomenon.

[ascl:2302.004] SFQEDtoolkit: Strong-field QED processes modeling for PIC and Monte Carlo codes

SFQEDtoolkit implements strong-field QED (SFQED) processes in existing particle-in-cell (PIC) and Monte Carlo codes to determine the dynamics of particles and plasmas in extreme electromagnetic fields, such as those present in the vicinity of compact astrophysical objects. The code uses advanced function approximation techniques to calculate high-energy photon emission and electron-positron pair creation probability rates and energy distributions within the locally-constant-field approximation (LCFA) as well as with more advanced models.

[ascl:2302.003] PHOTOe: Monte Carlo model for simulating the slowing down of photoelectrons

PHOTOe simulates the slowing down of photoelectrons in a gas with arbitrary amounts of H, He and O atoms, and thermal electrons, making PHOTOe useful for investigating the atmospheres of exoplanets. The multi-score scheme used in this code differs from other Monte Carlo approaches in that it efficiently handles rare collisional channels, as in the case of low-abundance excited atoms that undergo superelastic and inelastic collisions. PHOTOe outputs include production and energy yields, steady-state photoelectron flux, and estimates of the 'relaxation' time required by the photoelectrons to slow down from the injection energy to the cutoff energy. The model can also estimate the pathlength travelled by the photoelectrons while relaxing.

[ascl:2302.002] deconfuser: Fast orbit fitting to directly imaged multi-planetary systems

Deconfuser performs fast orbit fitting to directly imaged multi-planetary systems. It quickly fits orbits to planet detections in 2D images and ensures that all orbits within a certain tolerance are found. The code also tests all groupings of detections by planets (which detection belongs to which planet), and ranks partitions of detections by planets by deciding which assignment of detection-to-planet best fits the data.

[ascl:2302.001] nicaea: NumerIcal Cosmology And lEnsing cAlculations

nicaea calculates cosmology and weak-lensing quantities and functions from theoretical models of the large-scale structure. Written in C, it can compute the Hubble parameter, distances, and geometry for background cosmology, and linear perturbations, including growth factor, transfer function, cluster mass function, and linear 3D power spectra. It also calculates fitting formulae for non-linear power spectra, emulators, and halo model for Non-linear evolution, and the HOD model for galaxy clustering. In addition, nicaea can compute quantities for cosmic shear such as the convergence power spectrum, second-order correlation functions and derived second-order quantities, and third-order aperture mass moment; it can also calculate CMB anisotropies via CAMB (ascl:1102.026).

[submitted] PREVIS: Python Request Engine for Virtual Interferometric Survey

PREVIS is a Python module that provides functions to help determine the observability of astronomical sources from long-baseline interferometers worldwide: VLTI (ESO, Chile) and CHARA (USA). PREVIS uses data from the Virtual Observatory (OV), such as magnitudes, Spectral Energy Distribution (SED), celestial coordinates or Gaia distances. Then, it compares the target brightness to the limiting magnitudes of each instrument to determine whether the target is observable with present performances. PREVIS includes main facilities at the VLTI with PIONIER (H band), GRAVITY (K band) and MATISSE (L, M, N bands), and at CHARA array with VEGA (V band), PAVO (R bands), MIRC (H band), CLIMB (K band) and CLASSIC (H, K bands). PREVIS also uses the V or G magnitudes to check the guiding restriction or the tip/tilt correction limit. For the VLTI: if the star is too faint in G mag, PREVIS will look for the list of stars around the target (57 arcsec) with the appropriate magnitude and give the list of celestial coordinates usable as the guiding star.

[ascl:2301.030] HIPP: HIgh-Performance Package for scientific computation

HIPP (HIgh-Performance Package for scientific computation) provides elegant interfaces for some well-known HPC libraries. Some libraries are wrapped with full-OOP interfaces, and many new extensions based on those raw-interfaces are also provided. This C++ toolkit for HPC can significantly reduce the length of your code, making programming more productive.

[ascl:2301.029] ALMA3: plAnetary Love nuMbers cAlculator

ALMA3 computes loading and tidal Love numbers for a spherically symmetric, radially stratified planet. Both real (time-domain) and complex (frequency-domain) Love numbers can be computed. The planetary structure can include an arbitrary number of layers, and each layer can have a different rheological law. ALMA3 can model numerous linear rheologies, including Elastic, Maxwell visco-elastic, Newtonian viscous fluid, Kelvin-Voigt solid, Burgers and Andrade transient rheologies.

[ascl:2301.028] special: SPEctral Characterization of directly ImAged Low-mass companions

special (SPEctral Characterization of directly ImAged Low-mass companions) characterizes low-mass (M, L, T) dwarfs down to giant planets at optical/IR wavelengths. It can also be used more generally to characterize any type of object with a measured spectrum, provided a relevant input model grid, regardless of the observational method used to obtain the spectrum (direct imaging or not) and regardless of the format of the spectra (multi-band photometry, low-resolution or medium-resolution spectrum, or a combination thereof). It analyzes measured spectra, calculating the spectral correlation between channels of an IFS datacube and empirical spectral indices for MLT-dwarfs. It fits input spectra to either photo-/atmospheric model grids or a blackbody model, including additional parameters such as (extra) black body component(s), extinction and total-to-selective extinction ratio, and can use emcee (ascl:1303.002), nestle (ascl:2103.022), or UltraNest (ascl:1611.001) samplers infer posterior distributions on spectral model parameters in a Bayesian framework, among other tasks.

[ascl:2301.027] Puri-Psi: Radio interferometric imaging

Puri-Psi addresses radio interferometric imaging problems using state-of-the-art optimization algorithms and deep learning. It performs scalable monochromatic, wide-band, and polarized imaging. It also provide joint calibration and imaging, and scalable uncertainty quantification. A scalable framework for wide-field monochromatic intensity imaging is also available, which encompasses a pure optimization algorithm, as well as an AI-based method in the form of a plug-and-play algorithm propelled by Deep Neural Network denoisers.

[ascl:2301.026] MGwave: Detect kinematic moving groups in astronomical data

The 2-D wavelet transformation code MGwave detects kinematic moving groups in astronomical data; it can also investigate underdensities which can eventually provide further information about the MW's non-axisymmetric features. The code creates a histogram of the input data, then performs the wavelet transformation at the specified scales, returning the wavelet coefficients across the entire histogram in addition to information about the detected extrema. MGwave can also run Monte Carlo simulations to propagate uncertainties. It runs the wavelet transformation on simulated data (pulled from Gaussian distributions) many times and tracks the percentage of the simulations in which a given extrema is detected. This quantifies whether a detected overdensity or underdensity is robust to variations of the data within the provided errors.

[ascl:2301.025] desitarget: Selecting DESI targets from photometric catalogs

desitarget selects targets for spectroscopic follow-up by Dark Energy Spectroscopic Instrument (DESI). The pipeline uses bitmasks to record that a specific source has been selected by a particular targeting algorithm, setting bit-values in output data files in a number of different columns that indicate whether a particular target meets specific selection criteria. desitarget also outputs a unique TARGETID that allows each target to be tracked throughout the DESI survey. This TARGETID encodes information about each DESI target, such as the catalog the target was selected from, whether a target is a sky location or part of a random catalog, and whether a target is part of a secondary program.

[submitted] nFITSview: A simple and user-friendly FITS image viewer

nFITSview is a simple, user-friendly and open-source FITS image viewer available for Linux and Windows. One of the main concepts of nFITSview is to provide an intuitive user interface which may be helpful both for scientists and for amateur astronomers. nFITSview has different color mapping and manipulation schemes, supports different formats of FITS data files as well as exporting them to different popular image formats. It also supports command-line exporting (with some restrictions) of FITS files to other image formats.
The application is written in C++/Qt for achieving better performance, and with every next version the performance aspect is taken into account.
nFITSview uses its own libnfits library (can be used separately as well) for parsing the FITS files.

[ascl:2301.024] SOXS: Simulated Observations of X-ray Sources

SOXS creates simulated X-ray observations of astrophysical sources. The package provides a comprehensive set of tools to design source models and convolve them with simulated models of X-ray observatories. In particular, SOXS is the primary simulation tool for simulations of Lynx and Line Emission Mapper observations. SOXS provides facilities for creating spectral models, simple spatial models for sources, astrophysical background and foreground models, as well as a Python implementation of the SIMPUT file format.

[ascl:2301.023] PoWR: Potsdam Wolf-Rayet Models

PoWR (Potsdam Wolf-Rayet Models) calculates synthetic spectra for Wolf-Rayet and OB stars from model atmospheres which account for Non-LTE, spherical expansion and metal line blanketing. The model data is provided through a web interface and includes Spectral Energy Distribution, line spectrum in high resolution for different wavelength bands, and atmosphere stratification. For Wolf-Rayet stars of the nitrogen subclass, there are grids of hydrogen-free models and of models with a specified mass fraction of hydrogen. The iron-group and total CNO mass fractions correspond to the metallicity of the Galaxy, the Large Magellanic Cloud, or the Small Magellanic Cloud, respectively. The source code is available as a tarball on the same web interface.

[ascl:2301.022] GalCEM: GALactic Chemical Evolution Model

GalCEM (GALactic Chemical Evolution Model) tracks isotope masses as a function of time in a given galaxy. The list of tracked isotopes automatically adapts to the complete set provided by the input yields. The prescription includes massive stars, low-to-intermediate mass stars, and Type Ia supernovae as enrichment channels. Multi-dimensional interpolation curves are extracted from the input yield tables with a preprocessing tool; these interpolation curves improve the computation speeds of the full convolution integrals, which are computed for each isotope and for each enrichment channel. GalCEM also provides tools to track the mass rate change of individual isotopes on a typical spiral galaxy with a final baryonic mass of 5×1010M⊙.

[ascl:2301.021] WALDO: Waveform AnomaLy DetectOr

WALDO (Waveform AnomaLy DetectOr) flags possible anomalous Gravitational Waves from Numerical Relativity catalogs using deep learning. It uses a U-Net architecture to learn the waveform features of a dataset. After computing the mismatch between those waveforms and the neural predictions, WALDO isolates high mismatch evaluations for anomaly search.

[ascl:2301.020] VDA: Void Dwarf Analyzer

void-dwarf-analysis analyzes Keck Cosmic Web Imager datacubes to produce maps of kinematic properties (velocity and velocity dispersion), emission line fluxes, and gas-phase metallicities of void dwarf galaxies.

[ascl:2301.019] KCWI_DRP: Keck Cosmic Web Imager Data Reduction Pipeline in Python

KCWI_DRP, written in Python and based on kderp (ascl:2301.018), is the official DRP for the Keck Cosmic Web Imager at the W. M. Keck Observatory. It provides all of the functionality of the older pipeline and has three execution modes: multi-threading for CPU intensive tasks such as wavelength calibration, and multi-processing for large datasets. It offers vacuum to air and heliocentric or barycentric correction and the ability to use KOA file names or original file names. KCWI_DRP also improves the provenance and traceability of DRP versions and execution steps in the headers over kderp, and has versatile sky subtraction modes including using external sky frames and ability of masking regions.

[ascl:2301.018] kderp: Keck Cosmic Web Imager Data Extraction and Reduction Pipeline in IDL

kderp (KCWI Data Extraction and Reduction Pipeline) reduces data for the Keck Cosmic Web Imager. Written in IDL, it performs basic CCD reduction on raw images to produce bias and overscan subtracted, gain-corrected, trimmed and cosmic ray removed images; it can also subtract the sky. It defines the geometric transformations required to map each pixel in the 2d image into slice, postion, and wavelength, and performs flat field and illumination corrections. It generates cubes, applying the transformations previously solved to the object intensity, variance and mask images output from any of the previous stages, and uses a standard star observation to generate an inverse sensitivity curve which is applied to the corresponding observations to flux calibrate them.

This pipeline has been superseded by KCWI_DRP (ascl:2301.019).

[ascl:2301.017] ReACT: Calculation of non-linear power spectra from non-standard physics

ReACT extends the Copter (ascl:1304.022) and MG-Copter packages, which calculate redshift and real space large scale structure observables for a wide class of gravity and dark energy models. Additions to Copter include spherical collapse in modified gravity, halo model power spectrum for general theories, and real and redshift space LSS 2 point statistics for modified gravity and dark energy. ReACT also includes numerical perturbation theory kernel solvers, real space bispectra in modified gravity, and a numerical perturbation theory kernel solver up to 4th order for 1-loop bispectrum.

[ascl:2301.016] FERRE: Match physical models to measurements

FERRE matches physical models to observed data, taking a set of observations and identifying the model parameters that best reproduce the data, in a chi-squared sense. It solves the common problem of having numerical parametric models that are costly to evaluate and need to be used to interpret large data sets. FERRE provides flexibility to search for all model parameters, or hold constant some of them while searching for others. The code is written to be truly N-dimensional and fast. Model predictions are to be given as an array whose values are a function of the model parameters, i.e., numerically. FERRE holds this array in memory, or in a direct-access binary file, and interpolates in it. The code returns, in addition to the optimal set of parameters, their error covariance, and the corresponding model prediction. The code is written in FORTRAN90.

[ascl:2301.015] SOAP-GPU: Spectral time series simulations with GPU

SOAP-GPU is a revision of SOAP 2 (ascl:1504.021), which simulates spectral time series with the effect of active regions (spot, faculae or both). In addition to the traditional outputs of SOAP 2.0 (the cross-correlation function and extracted parameters: radial velocity, bisector span, full width at half maximum), SOAP-GPU generates the integrated spectra at each phase for given input spectra and spectral resolution. Additional capabilities include fast spectral simulation of stellar activity due to GPU acceleration, simulation of more complicated active region structures with superposition between active regions, and more realistic line bisectors, based on solar observations, that varies as function of mu angle for both quiet and active regions. In addition, SOAP-GPU accepts any input high resolution observed spectra. The PHOENIX synthetic spectral library are already implemented at the code level which allows users to simulate stellar activity for stars other than the Sun. Furthermore, SOAP-GPU simulates realistic spectral time series with either spot number/SDO image as additional inputs. The code is written in C and provides python scripts for input pre-processing and output post-processing.

[ascl:2301.014] LBL: Line-by-line velocity measurements

LBL derives velocity measurements from high-resolution (R>50 000) datasets by accounting for outliers in the spectra data. It is tailored for fiber-fed multi-order spectrographs, both in optical and near-infrared (up to 2.5µm) domains. The domain is split into individual units (lines) and the velocity and its associated uncertainty are measured within each line and combined through a mixture model to allow for the presence of spurious values. In addition to the velocity, other quantities are also derived, the most important being a value (dW) that can be understood (for a Gaussian line) as a change in the line FWHM. These values provide useful stellar activity indicators. LBL works on data from a variety of instruments, including SPIRou, NIRPS, HARPS, and ESPRESSO. The code's output is an rdb table that can be uploaded to the online DACE pRV analysis tool.

[ascl:2301.013] pyExoRaMa: An interactive tool to investigate the radius-mass diagram for exoplanets

pyExoRaMa visualizes and manipulates data related to exoplanets and their host stars in a multi-dimensional parameter space. It enables statistical studies based on the large and constantly increasing number of detected exoplanets, identifies possible interdependence among several physical parameters, and compares observables with theoretical models describing the exoplanet composition and structure.

[ascl:2301.012] XGA: Efficient analysis of XMM observations

XGA (X-ray: Generate and Analyse) analyzes X-ray sources observed by the XMM-Newton Space telescope. It is based around declaring different types of source and sample objects which correspond to real X-ray sources, finding all available data, and then insulating the user from the tedious generation and basic analysis of X-ray data products. XGA generates photometric products and spectra for individual sources, or whole samples, with just a few lines of code. Though not a pipeline, pipelines for complex analysis can be built on top of it. XGA provides an easy to use (and parallelized) Python interface with XMM's Science Analysis System (ascl:1404.004), as well as with XSPEC (ascl:9910.005). All XMM products and fit results are read into an XGA source storage structure, thus avoiding the need to leave a Python environment at any point during the analysis. This module also supports more complex analyses for specific object types such as the easy generation of scaling relations, the measurement of gas masses for galaxy clusters, and the PSF correction of images.

[ascl:2301.011] Rosetta: Platform for resource-intensive, interactive data analysis

Rosetta runs tasks for resource-intensive, interactive data analysis as software containers. The code's architecture frames user tasks as microservices – independent and self-contained units – which fully support custom and user-defined software packages, libraries and environments. These include complete remote desktop and GUI applications, common analysis environments such as the Jupyter Notebooks. Rosetta relies on Open Container Initiative containers, allowing for safe, effective and reproducible code execution. It can use a number of container engines and runtimes and seamlessly supports several workload management systems, thus enabling containerized workloads on a wide range of computing resources.

[ascl:2301.010] Fastcc: Broadband radio telescope receiver fast color corrections

Fastcc returns color corrections for different spectra for various Cosmic Microwave Background experiments. Available in both Python and IDL, the script is easy to use when analyzing radio spectra of sources with data from multiple wide-survey CMB experiments in a consistent way across multiple experiments.

[ascl:2301.009] Xpol: Pseudo-Cl power spectrum estimator

Xpol computes angular power spectra based on cross-correlation between maps and covariance matrices. The code is written in C and is fully MPI parallelized in CPU and memory using spherical transform by s2hat (ascl:1110.013). It has been used to derive CMB and dust power spectra for Archeops and CMB, dust, CIB, SZ, SZ-CIB for Planck, among others.

[ascl:2301.008] HiLLiPoP: High-L Likelihood Polarized for Planck

HiLLiPoP is a multifrequency CMB likelihood for Planck data. The likelihood is a spectrum-based Gaussian approximation for cross-correlation spectra from Planck 100, 143 and 217GHz split-frequency maps, with semi-analytic estimates of the Cl covariance matrix based on the data. The cross-spectra are debiased from the effects of the mask and the beam leakage using Xpol (ascl:2301.009) before being compared to the model, which includes CMB and foreground residuals. They cover the multipoles from ℓ=30 to ℓ=2500. HiLLiPoP is interfaced with the Cobaya (ascl:1910.019) MCMC sampler.

[ascl:2301.007] LoLLiPoP: Low-L Likelihood Polarized for Planck

LoLLiPoP is a Planck low-l polarization likelihood based on cross-power-spectra for which the bias is zero when the noise is uncorrelated between maps. It uses a modified approximation to apply to cross-power spectra and is interfaced with the Cobaya (ascl:1910.019) MCMC sampler. Cross-spectra are computed on the CMB maps from Commander component separation applied on each detset-split Planck frequency maps.

[ascl:2301.006] Self-cal: Optical/IR long-baseline interferometry

Self-cal produces radio-interferometric images of an astrophysical object. The code is an adaptation of the self-calibration algorithm to optical/infrared long-baseline interferometry, especially to make use of differential phases and differential visibilities. It works together with the Mira image reconstruction software and has been used mainly on VLTI data. Self-cal, written in Yorick, is also available as part of fitsOmatic (ascl:2301.005).

[ascl:2301.005] fitOmatic: Interferometric data modeling

The fitOmatic model-fitting prototyping tool tests multi-wavelength model-fitting and exploits VLTI data. It provides tools to define simple geometrical models and conveniently adjust the model's parameters. Written in Yorick, it takes optical interferometry FITS (oifits) files as input and allows the user to define a model of the source from a set of pre-defined models, which can be combined to make more complicated models. fitOmatic then computes the Fourier Transform of the modeled brightness distribution and synthetic observables are computed at the wavelengths and projected baselines of the observations. fitomatic's strength is its ability to define vector-parameters, i.e., parameters that may depend on wavelength and/or time. The self-cal (ascl:2301.006) component of fitOmatic is also available as a separate code.

[ascl:2301.004] HEADSS: HiErArchical Data Splitting and Stitching for non-distributed clustering algorithms

HEADSS (HiErArchical Data Splitting and Stitching) facilitates clustering at scale, unlike clustering algorithms that scale poorly with increased data volume or that are intrinsically non-distributed. HEADSS automates data splitting and stitching, allowing repeatable handling, and removal, of edge effects. Implemented in conjunction with scikit's HDBSCAN, the code achieves orders of magnitude reduction in single node memory requirements for both non-distributed and distributed implementations, with the latter offering similar order of magnitude reductions in total run times while recovering analogous accuracy. HEADSS also establishes a hierarchy of features by using a subset of clustering features to split the data.

[ascl:2301.003] WF4Py: Gravitational waves waveform models in pure Python language

WF4Py implements frequency-domain gravitational wave waveform models in pure Python, thus enabling parallelization over multiple events at a time. Waveforms in WF4Py are built as classes; the functions take dictionaries containing the parameters of the events to analyze as input and provide Fourier domain waveform models. All the waveforms are accurately checked with their implementation in LALSuite (ascl:2012.021) and are a core element of GWFAST (ascl:2212.001).

[ascl:2301.002] Pyxel: Detector and end-to-end instrument simulation

Pyxel hosts and pipelines models (analytical, numerical, statistical) simulating different types of detector effects on images produced by Charge-Coupled Devices (CCD), Monolithic, and Hybrid CMOS imaging sensors. Users can provide one or more input images to Pyxel, set the detector and model parameters, and select which effects to simulate, such as cosmic rays, detector Point Spread Function (PSF), electronic noises, Charge Transfer Inefficiency (CTI), persistence, dark current, and charge diffusion, among others. The output is one or more images including the simulated detector effects combined. The Pyxel framework, written in Python, provides basic image analysis tools, an input image generator, and a parametric mode to perform parametric and sensitivity analysis. It also offers a model calibration mode to find optimal values of its parameters based on a target dataset the model should reproduce.

[ascl:2301.001] CALSAGOS: Select cluster members and search, find, and identify substructures

CALSAGOS (Clustering ALgorithmS Applied to Galaxies in Overdense Systems) selects cluster members and searches, finds, and identifies substructures and galaxy groups in and around galaxy clusters using the redshift and position in the sky of the galaxies. The package offers two ways to determine cluster members, ISOMER and CLUMBERI. The ISOMER (Identifier of SpectrOscopic MembERs) function selects the spectroscopic cluster members by defining cluster members as those galaxies with a peculiar velocity lower than the escape velocity of the cluster. The CLUMBERI (CLUster MemBER Identifier) function select the cluster members using a 3D-Gaussian Mixture Modules (GMM). Both functions remove the field interlopers by using a 3-sigma clipping algorithm. CALSAGOS uses the function LAGASU (LAbeller of GAlaxies within SUbstructures) to search, find, and identify substructures and groups in and around a galaxy cluster; this function is based on clustering algorithms (GMM and DBSCAN), which search areas with high density to define a substructure or groups.

[submitted] unWISE-verse: An Integrated WiseView and Zooniverse Data Pipeline

unWISE-verse is an integrated Python pipeline for downloading sets of unWISE time-resolved coadd cutouts from the WiseView image service and uploading subjects to Zooniverse.org for use in astronomical citizen science research. This software was initially designed for the Backyard Worlds: Cool Neighbors research project and is optimized for target sets containing low luminosity brown dwarf candidates. However, unWISE-verse can be applied to other future astronomical research projects that seek to make use of unWISE infrared sky maps, such as studies of infrared variable/transient sources.

[ascl:2212.026] Spender: Neural spectrum encoder and decoder

Spender establishes a restframe for galaxy spectra that has higher resolution and larger wavelength range than the spectra from which it is trained. The model can be trained from spectra at different redshifts or even from different instruments without the need to standardize the observations. Spender also has an explicit, differentiable redshift dependence, which can be coupled with a redshift estimator for a fully data-driven spectrum analysis pipeline. The code describes the restframe spectrum by an autoencoder and transforms the restframe model to the observed redshift; it also matches the spectral resolution and line spread function of the instrument.

[ascl:2212.025] CONTROL: Colorado Ultraviolet Transit Experiment data reduction pipeline

CONTROL (CUTE autONomous daTa ReductiOn pipeLine) produces science-quality output with a single command line with zero user interference for CUTE (Colorado Ultraviolet Transit Experiment) data. It can be used for any single order spectral data in any wavelength without any modification. The pipeline is governed by a parameter file, which is available with this distribution. CONTROL is fully automated and works in a series of steps following standard CCD reduction techniques. It creates a reduction log to track processes carried out and any parameters used.

[ascl:2212.024] Burning Arrow: Black hole massive particles orbit degradation

Burning Arrow determines the destabilization of massive particle circular orbits due to thermal radiation, emitted in X-ray, from the hot accretion disk material. This code requires the radiation forces exerted on the material at the point of interest found by running the code Infinity (ascl:2212.021). Burning Arrow begins by assuming a target particle in the disk that moves in a circular orbit. It then introduces the recorded radiation forces from Infinity code for the target region. The forces are subsequently introduced into the target particle equations of motion and the trajectory is recalculated. Burning Arrow then produces images of the black hole - accretion disk system that includes the degenerated particle trajectories that obey the assorted velocity profiles.

[ascl:2212.023] Tranquillity: Creating black hole spin divergence plots

Tranquillity creates an observing screen looking toward a black hole - accretion disk system, seeks the object, then searches and locates its contour. Subsequently, it attempts to locate the first Einstein "echo" ring and its location. Finally, it collates the retrieved information and draws conclusions; these include the accretion disk level inclination compared to the line of sight and the main disk and the first echo median. The displacement, and thus the divergence of the latter two, is the required information in order to construct the divergence plots. Other programs can later on automatically read these plots and provide estimations of the central black hole spin.

[ascl:2212.022] Elysium: Observing black hole accretion disks

Elysium creates an observing screen at the desirable distance away from a black hole system. Observers set on every pixel of this screen then photograph the area toward the black hole - accretion disk system and report back what they record. This can be the accretion disk (incoming photons bring in radiation and thus energy), the black hole event horizon, or the empty space outside and beyond the system (there are no incoming photons or energy). The central black hole can be either Schwarzschild (nonrotating) or Kerr (rotating) by choice of the user.

[ascl:2212.021] Infinity: Calculate accretion disk radiation forces onto moving particles

Infinity sets an observer in a black hole - accretion disk system. The black hole can be either Schwarzschild (nonrotating) or Kerr (rotating) by choice of the user. This observer can be on the surface of the disk, in its exterior or its interior (if the disk is not opaque). Infinity then scans the entire sky around the observer and investigates whether photons emitted by the hot accretion disk material can reach them. After recording the incoming radiation, the program calculates the stress-energy tensor of the radiation. Afterwards, the program calculates the radiation flux and hence, the radiation force exerted on target particles of various velocity profiles.

[ascl:2212.020] Omega: Photon equations of motion

Omega solves the photon equations of motion in the environment surrounding a black hole. This black hole can be either Schwarzschild (nonrotating) or Kerr (rotating) by choice of the user. The software offers numerous options, such as the geometrical setup of the accretion disk around the black hole (including no disk, band, slab, wedge, among others, the spin parameter of the central black hole, and the thickness of the accretion disk. Other options that can be set includ the azimuthal angle of the photon emission/reception, the poloidal angle of the photon emission/reception, and how far away or close to the system to look.

[ascl:2212.019] m2mcluster: Star clusters made-to-measure modeling

m2mcluster performs made-to-measure modeling of star clusters, and can fit target observations of a Galactic globular cluster's 3D density profile and individual kinematic properties, including proper motion velocity dispersion, and line of sight velocity dispersion. The code uses AMUSE (ascl:1107.007) to model the gravitational N-body evolution of the system between time steps; GalPy (ascl:1411.008) is also required.

[ascl:2212.018] SourceXtractor++: Extracts sources from astronomical images

SourceXtractor++ extracts a catalog of sources from astronomical images; it is the successor to SExtractor (ascl:1010.064). SourceXtractor++ has been completely rewritten in C++ and improves over its predecessor in many ways. It provides support for multiple “measurement” images, has an optimized multi-object, multi-frame model-fitting engine, and can define complex priors and dependencies for model parameters. It also offers efficient image data caching and multi-threaded processing, and has a modular design with support for third-party plug-ins.

[ascl:2212.017] powspec: Power and cross spectral density of 2D arrays

powspec provides functions to compute power and cross spectral density of 2D arrays. Units are properly taken into account. It can, for example, create fake Gaussian field images, compute power spectra P(k) of each image, shrink a mask with regard to a kernel, generate a Gaussian field, and plot various results.

[ascl:2212.016] AbundanceMatching: Subhalo abundance matching with scatter

The AbundanceMatching Python module creates (interpolates and extrapolates) abundance functions and also provides fiducial deconvolution and abundance matching.

[ascl:2212.015] SImMER: Stellar Image Maturation via Efficient Reduction

SImMER (Stellar Image Maturation via Efficient Reduction) reduces astronomical imaging data. It performs standard dark-subtraction and flat-fielding operations on data from, for example, the ShARCS camera on the Shane 3-m telescope at Lick Observatory and the PHARO camera on the Hale 5.1-m telescope at Palomar Observatory; its object-oriented design allows the software to be extended to other instruments. SImMER can also perform sky-subtraction, image registration, FWHM measurement, and contrast curve calculation, and can generate tables and plots. For widely separated stars which are of somewhat equal brightness, a “wide binary” mode allows the user to selects which star is the primary around which each image should be centered.

[ascl:2212.014] pyTANSPEC: Python tool for extracting 1D TANSPEC spectra from 2D images

pyTANSPEC extracts XD-mode spectra automatically from data collected by the TIFR-ARIES Near Infrared Spectrometer (TANSPEC) on India's ground-based 3.6-m Devasthal Optical Telescope at Nainital, India. The TANSPEC offers three modes of observations, imaging with various filters, spectroscopy in the low-resolution prism mode with derived R~ 100-400 and the high-resolution cross-dispersed mode (XD-mode) with derived median R~ 2750 for a slit of width 0.5 arcsec. In the XD-mode, ten cross-dispersed orders are packed in the 2048 x 2048 pixels detector to cover the full wavelength regime. The XD-mode is most utilized; pyTANSPEC provides a dedicated pipeline for consistent data reduction for all orders and to reduces data reduction time. The code requires nominal human intervention only for the quality assurance of the reduced data. Two customized configuration files are used to guide the data reduction. The pipeline creates a log file for all the fits files in a given data directory from its header, identifies correct frames (science, continuum and calibration lamps) based on the user input, and offers an option to the user for eyeballing and accepting/removing of the frames, does the cleaning of raw science frames and yields final wavelength calibrated spectra of all orders simultaneously.

[ascl:2212.013] PACMAN: Planetary Atmosphere, Crust, and MANtle geochemical evolution

PACMAN (Planetary Atmosphere, Crust, and MANtle geochemical evolution) runs a coupled redox-geochemical-climate evolution model. It runs Monte Carlo calculations over nominal parameter ranges, including number of iterations and number of cores for parallelization, which can be altered to reproduce different scenarios and sensitivity tests. Model outputs and corresponding input parameters are saved in separate files which are used to plot results; the the user can choose which outputs to plot, including all successful outputs, nominal Earth outputs, waterworld false positives, desertworld false positives, and high CO2:H2O false positives. Among other functions, PACMAN contains functions for interpolating the pre-computed Outgoing Longwave Radiation (OLR) grid, the atmosphere-ocean partitioning grid, and the stratospheric water vapor grid, calculating bond albedo and outgassing fluxes.

[ascl:2212.012] BANZAI-NRES: BANZAI data reduction pipeline for NRES

The BANZAI-NRES pipeline processes data from the Network of Robotic Echelle Spectrographs (NRES) on the Las Cumbres Observatory network and provides extracted, wavelength calibrated spectra. If the target is a star, it provides stellar classification parameters (e.g., effective temperature and surface gravity) and a radial velocity measurement. The automated radial velocity measurements from this pipeline have a precision of ~ 10 m/s for high signal-to-noise observations. The data flow and infrastructure of this code relies heavily on BANZAI (ascl:2207.031), enabling BANZAI-NRES to focus on analysis that is specific to spectrographs. The wavelength calibration is primarily done using xwavecal (ascl:2212.011). The pipeline propagates an estimate of the formal uncertainties from all of the data processing stages and includes these in the output data products. These are used as weights in the cross correlation function to measure the radial velocity.

[ascl:2212.011] xwavecal: Wavelength calibrating echelle spectrographs

The xwavecal library automatically wavelength calibrates echelle spectrographs for high precision radial velocity work. The routines are designed to operate on data with extracted 1D spectra. The library provides a convienience function which returns a list of wavelengths from just a list of spectral feature coordinates (pixel and order) and a reference line list. The returned wavelengths are the wavelengths of the measured spectral features under the best fit wavelength model. xwavecal also provides line identification and spectral reduction utilities. The library is modular; each step of the wavelength calibration is a stage which can be disabled by removing the associated line in the config.ini file. Wavelength calibrating data which already have spectra means only using the wavelength calibration stages. Using the full experimental pipeline means enabling the other data reduction stages, such as overscan subtraction.

[ascl:2212.010] sf_deconvolve: PSF deconvolution and analysis

sf_deconvolve performs PSF deconvolution using a low-rank approximation and sparsity. It can handle a fixed PSF for the entire field or a stack of PSFs for each galaxy position. The code accepts Numpy binary files or FITS as input, takes the observed (i.e. with PSF effects and noise) stack of galaxy images and a known PSF, and attempts to reconstruct the original images. sf_deconvolve can be run in a terminal or in an active Python session, and includes options for initialization, optimization, low-Rank approximation, sparsity, PSF estimation, and other attributes.

[ascl:2212.009] Hazma: Compute indirect detection constraints on sub-GeV dark matter

Hazma enables indirect detection of sub-GeV dark matter. It computes gamma-ray and electron/positron spectra from dark matter annihilations, sets limits on sub-GeV dark matter using existing gamma-ray data, and determines the discovery reach of future gamma-ray detectors. The code also derives accurate CMB constraints. Hazma comes with several sub-GeV dark matter models, for which it provides functions to compute dark matter annihilation cross sections and mediator decay widths. A variety of low-level tools are provided to make it straightforward to define new models.

[ascl:2212.008] panco2: Pressure profile measurements of galaxy clusters

panco2 extracts measurements of the pressure profile of the hot gas inside galaxy clusters from millimeter-wave observations. The extraction is performed using forward modeling the millimeter-wave signal of clusters and MCMC sampling of a posterior distribution for the parameters given the input data. Many characteristic features of millimeter-wave observations can be taken into account, such as filtering (both through PSF smearing and transfer functions), point source contamination, and correlated noise.

[ascl:2212.007] PyMCCF: Python Modernized Cross Correlation Function for reverberation mapping studies

PyMCCF (Python Modernized Cross Correlation Function), also known as MCCF, cross correlates two light curves that are unevenly sampled using linear interpolation and measures the peak and centroid of the cross-correlation function. Based on PyCCF (ascl:1805.032) and ICCF, it introduces a new parameter, MAX, to reduce the number of interpolated points used to just those which are not farther from the nearest real one than the MAX. This significantly reduces noise from interpolation errors. The estimation of the errors in PyMCCF is exactly the same as in PyCCF.

[ascl:2212.006] GPry: Bayesian inference of expensive likelihoods with Gaussian processes

GPry efficiently obtains marginal quantities from computationally expensive likelihoods. It works best with smooth (continuous) likelihoods and posteriors that are slow to converge by other methods, which is dependent on the number of dimensions and expected shape of the posterior distribution. The likelihood should be low-dimensional (d<20 as a rule of thumb), though the code may still provide considerable improvements in speed in higher dimensions, despite an increase in the computational overhead of the algorithm. GPry is an alternative to samplers such as MCMC and Nested Sampling with a goal of speeding up inference in cosmology, though the software will work with any likelihood that can be called as a python function. It uses Cobaya's (ascl:1910.019) model framework so all of Cobaya's inbuilt likelihoods work, too.

[ascl:2212.005] MTNeedlet: Spherical maps filtering

MTNeedlet uses needlets to filter spherical (Healpix) maps and detect and analyze the maxima population using a multiple testing approach. It has been developed with the CMB in mind, but it can be applied to other spherical maps. It pivots around three basic steps: 1.) The calculation of several types of needlets and their possible use to filter maps; 2.) The detection of maxima (or minima) on spherical maps, their visualization and basic analysis; and 3.) The multiple testing approach in order to detect anomalies in the maxima population of the maps with respect to the expected behavior for a random Gaussian map. MTNeedlet relies on Healpy (ascl:2008.022) to efficiently deal with spherical maps.

[ascl:2212.004] FastDF: Integrating neutrino geodesics in linear theory

FastDF (Fast Distribution Function) integrates relativistic particles along geodesics in a comoving periodic volume with forces determined by cosmological linear perturbation theory. Its main application is to set up accurate particle realizations of the linear phase-space distribution of massive relic neutrinos by starting with an analytical solution deep in radiation domination. Such particle realizations are useful for Monte Carlo experiments and provide consistent initial conditions for cosmological N-body simulations. Gravitational forces are calculated from three-dimensional potential grids, which are obtained by convolving random phases with linear transfer functions using Fast Fourier Transforms. The equations of motion are solved using a symplectic leapfrog integration scheme to conserve phase-space density and prevent the build-up of errors. Particles can be exported in different gauges and snapshots are provided in the HDF5 format, compatible with N-body codes like SWIFT (ascl:1805.020) and Gadget-4 (ascl:2204.014). The code has an interface with CLASS (ascl:1106.020) for calculating transfer functions and with monofonIC (ascl:2008.024) for setting up initial conditions with dark matter, baryons, and neutrinos.

[ascl:2212.003] MGCosmoPop: Modified gravity and cosmology with binary black holes population models

MGCosmoPop implements a hierarchical Bayesian inference method for constraining the background cosmological history, in particular the Hubble constant, together with modified gravitational-wave propagation and binary black holes population models (mass, redshift and spin distributions) with gravitational-wave data. It includes support for loading and analyzing data from the GWTC-3 catalog as well as for generating injections to evaluate selection effects, and features a module to run in parallel on clusters.

[ascl:2212.002] Eventdisplay: Analysis and reconstruction package for ground-based Gamma-ray astronomy

Eventdisplay reconstructs and analyzes data from the Imaging Atmospheric Cherenkov Telescopes (IACT). It has been primarily developed for VERITAS and CTA analysis. The package calibrates and parametrizes images, event reconstruction, and stereo analysis, and provides train boosted decision trees for direction and energy reconstruction. It fills and uses lookup tables for mean scaled width and length calculation, energy reconstruction, and stereo reconstruction, and calculates radial camera acceptance from data files and instrument response functions such as effective areas, angular point-spread function, and energy resolution. Eventdisplay offers additional tools as well, including tools for calculating sky maps and spectral energy distribution, and to plot instrument response function, spectral energy distributions, light curves, and sky maps, among others.

[ascl:2212.001] GWFAST: Fisher information matrix python package for gravitational-wave detectors

GWFAST forecasts the signal-to-noise ratios and parameter estimation capabilities of networks of gravitational-wave detectors, based on the Fisher information matrix approximation. It is designed for applications to third-generation gravitational-wave detectors. It is based on Automatic Differentiation, which makes use of the library JAX (ascl:2111.002). This allows efficient parallelization and numerical accuracy. The code includes a module for parallel computation on clusters.

[submitted] SLEPLET

Many fields in science and engineering measure data that inherently live on non-Euclidean geometries, such as the sphere. Techniques developed in the Euclidean setting must be extended to other geometries. Due to recent interest in geometric deep learning, analogues of Euclidean techniques must also handle general manifolds or graphs. Often, data are only observed over partial regions of manifolds, and thus standard whole-manifold techniques may not yield accurate predictions. In this thesis, a new wavelet basis is designed for datasets like these.

Although many definitions of spherical convolutions exist, none fully emulate the Euclidean definition. A novel spherical convolution is developed, designed to tackle the shortcomings of existing methods. The so-called sifting convolution exploits the sifting property of the Dirac delta and follows by the inner product of a function with the translated version of another. This translation operator is analogous to the Euclidean translation in harmonic space and exhibits some useful properties. In particular, the sifting convolution supports directional kernels; has an output that remains on the sphere; and is efficient to compute. The convolution is entirely generic and thus may be used with any set of basis functions. An application of the sifting convolution with a topographic map of the Earth demonstrates that it supports directional kernels to perform anisotropic filtering.

Slepian wavelets are built upon the eigenfunctions of the Slepian concentration problem of the manifold - a set of bandlimited functions which are maximally concentrated within a given region. Wavelets are constructed through a tiling of the Slepian harmonic line by leveraging the existing scale-discretised framework. A straightforward denoising formalism demonstrates a boost in signal-to-noise for both a spherical and general manifold example. Whilst these wavelets were inspired by spherical datasets, like in cosmology, the wavelet construction may be utilised for manifold or graph data.

[ascl:2211.020] EXCEED-DM: EXtended Calculation of Electronic Excitations for Direct detection of Dark Matter

EXCEED-DM (EXtended Calculation of Electronic Excitations for Direct detection of Dark Matter) provides a complete framework for computing DM-electron interaction rates. Given an electronic configuration, EXCEED-DM computes the relevant electronic matrix elements, then particle physics specific rates from these matrix elements. This allows for separation between approximations regarding the electronic state configuration, and the specific calculation being performed.

[ascl:2211.019] APERO: A PipelinE to Reduce Observations

APERO (A PipelinE to Reduce Observations) performs data reduction for the Canada-France-Hawaii Telescope's near-infrared spectropolarimeter SPIRou and offers different recipes or modules for performing specific tasks. APERO can individually run recipes or process a set of files, such as cleaning a data file of detector effects, collecting all dark files and creating a master dark image to use for correction, and creating a bad pixel mask for identifying and dealing with bad pixels. It can extract out flat images to measure the blaze and produced blaze correction and flat correction images, extract dark frames to provide correction for the thermal background after extraction of science or calibration frames, and correct extracted files for leakage coming from a FP (for OBJ_FP files only). It can also take a hot star and calculate telluric transmission, and then use the telluric transmission to calculate principle components (PCA) for correcting input images of atmospheric absorption, among many other tasks.

[ascl:2211.018] ODNet: Asteroid occultation detection convolutional neural network

ODNet uses a convolutional neural network to examine frames of a given observation, using the flux of a targeted star along time, to detect occultations. This is particularly useful to reliably detect asteroid occultations for the Unistellar Network, which consists of 10,000 digital telescopes owned by citizen scientists that is regularly used to record asteroid occultations. ODNet is not costly in term of computing power, opening the possibility for embedding the code on the telescope directly. ODNet's models were developed and trained using TensorFlow version 2.4.

[ascl:2211.017] BiGONLight: Bi-local Geodesic Operators framework for Numerical Light propagation

BiGONLight (Bi-local geodesic operators framework for numerical light propagation) encodes the Bi-local Geodesic Operators formalism (BGO) to study light propagation in the geometric optics regime in General Relativity. The parallel transport equations, the optical tidal matrix, and the geodesic deviation equations for the bilocal operators are expressed in 3+1 form and encoded in BiGONLight as Mathematica functions. The bilocal operators are used to obtain all possible optical observables by combining them with the observer and emitter four-velocities and four-accelerations. The user can choose the position of the source and the observer anywhere along the null geodesic with any four-velocities and four-accelerations.

[ascl:2211.016] Korg: 1D local thermodynamic equilibrium stellar spectral synthesis

Korg computes stellar spectra from 1D model atmospheres and linelists assuming local thermodynamic equilibrium and implements both plane-parallel and spherical radiative transfer. The code is generally faster than other codes, and is compatible with automatic differentiation libraries and easily extensible, making it ideal for statistical inference and parameter estimation applied to large data sets.

[ascl:2211.015] H-FISTA: Phase retrieval for pulsar spectroscopy

H-FISTA (Hierarchical Fast Iterative Shrinkage Thresholding Algorithm) retrieves the phases of the wavefield from intensity measurements for pulsar spectroscopy. The code accepts input data in ASCII format as produced by PSRchive's (ascl:1105.014) psrflux function, a FITS file, or a pickle. If using a notebook, any custom reader can be used as long as the data ends up in a NumPy array. H-FISTA obtains sparse models of the wavefield in a hierarchical approach with progressively increasing depth. Once the tail of the noise distribution is reached, the hierarchy terminates with a final unregularized optimization, resulting in a fully dense model of the complex wavefield that permits the discovery of faint signals by appropriate averaging.

[ascl:2211.014] PDFchem: Average abundance of species from Av-PDFs

PDFchem models the cold ISM at moderate and large scales using functions connecting the quantities of the local and the observed visual extinctions and the local number density with probability density functions. For any given observed visual extinction sampled with thousands of clouds, the algorithm instantly computes the average abundances of the most important species and performs radiative transfer calculations to estimate the average emission of the most commonly observed lines.

[ascl:2211.013] 2DFFTUtils: 2DFFT Utilities implementation

The Python module 2DFFTUtils implements tasks associated with measuring spiral galaxy pitch angle with 2DFFT (ascl:1608.015). Since most of the 2DFFT utilities are implemented in one place, it makes preparing images for 2DFFT and dealing with 2DFFT data interactively or in scripts event easier.

[ascl:2211.012] gsf: Grism SED Fitting package

gsf fits photometric data points, simultaneously with grism spectra if provided, to get posterior probability of galaxy physical properties, such as stellar mass, dust attenuation, metallicity, as well as star formation and metallicity enrichment histories. Designed for extra-galactic science, this flexible, python-based SED fitting code involves a Markov-Chain Monte-Carlo (MCMC) process, and may take more time (depending on the number of parameters and length of MCMC chains) than other SED fitting codes based on chi-square minimization.

[ascl:2211.011] fastSHT: Fast Spherical Harmonic Transforms

fastSHT performs spherical harmonic transforms on a large number of spherical maps. It converts massive SHT operations to a BLAS level 3 problem and uses the highly optimized matrix multiplication toolkit to accelerate the computation. GPU acceleration is supported and can be very effective. The core code is written in Fortran, but a Python wrapper is provided and recommended.

[ascl:2211.010] BlackJAX: Library of samplers for JAX

BlackJAX is a sampling library designed for ease of use, speed, and modularity and works on CPU as well as GPU. It is not a probabilistic programming library (PLL), though it integrates well with PPLs as long as they can provide a (potentially unnormalized) log-probability density function compatible with JAX. BlackJAX is written in pure Python and depends on XLA via JAX (ascl:2111.002). It can be used by those who have a logpdf and need a sampler or need more than a general-purpose sampler. It is also useful for building a sample on GPU and for users who want to learn how sampling algorithms work.

[ascl:2211.009] ovejero: Bayesian neural network inference of strong gravitational lenses

ovejero conducts hierarchical inference of strongly-lensed systems with Bayesian neural networks. It requires lenstronomy (ascl:1804.012) and fastell (ascl:9910.003) to run lens models with elliptical mass distributions. The code trains Bayesian Neural Networks (BNNs) to predict posteriors on strong gravitational lensing images and can integrate with forward modeling tools in lenstronomy to allow comparison between BNN outputs and more traditional methods. ovejero also provides hierarchical inference tools to generate population parameter estimates and unbiased posteriors on independent test sets.

[ascl:2211.008] pmclib: Population Monte Carlo library

The Population Monte-Carlo (PMC) sampling code pmclib performs fast end efficient parallel iterative importance sampling to compute integrals over the posterior including the Bayesian evidence.

[ascl:2211.007] mgcnn: Standard and modified gravity (MG) cosmological models classifier

mgcnn is a Convolutional Neural Network (CNN) architecture for classifying standard and modified gravity (MG) cosmological models based on the weak-lensing convergence maps they produce. It is implemented in Keras using TensorFlow as the backend. The code offers three options for the noise flag, which correspond to noise standard deviations, and additional options for the number of training iterations and epochs. Confusion matrices and evaluation metrics (loss function and validation accuracy) are saved as numpy arrays in the generated output/ directory after each iteration.

[ascl:2211.006] baobab: Training data generator for hierarchically modeling strong lenses with Bayesian neural networks

baobab generates images of strongly-lensed systems, given some configurable prior distributions over the parameters of the lens and light profiles as well as configurable assumptions about the instrument and observation conditions. Wrapped around lenstronomy (ascl:1804.012), baobab supports prior distributions ranging from artificially simple to empirical. A major use case for baobab is the generation of training and test sets for hierarchical inference using Bayesian neural networks (BNNs); the code can generate the training and test sets using different priors.

[ascl:2211.005] unTimely_Catalog_explorer: A search and visualization tool for the unTimely Catalog

unTimely Catalog Explorer searches for and visualizes detections in the unTimely Catalog, a full-sky, time-domain catalog of detections based on WISE and NEOWISE image data acquired between 2010 and 2020. The tool searches the catalog by coordinates to create finder charts for each epoch with overplotted catalog positions and light curves using the unTimely photometry, to overplot these light curves with AllWISE multi-epoch and NEOWISE-R single exposure (L1b) photometry, and to create image blinks with overlaid catalog positions in GIF format.

[ascl:2211.004] PAHDecomp: Decomposing the mid-IR spectra of extremely obscured galaxies

PAHDecomp models mid-infrared spectra of galaxies; it is based on the popular PAHFIT code (ascl:1210.009). In contrast to PAHFIT, this model decomposes the continuum into a star-forming component and an obscured nuclear component based on Bayesian priors on the shape of the star-forming component (using templates + prior on extinction), making this tool ideally suited for modeling the spectra of heavily obscured galaxies. PAHDecomp successfully recovers properties of Compact Obscured Nuclei (CONs) where the inferred nuclear optical depth strongly correlates with the surface brightness of HCN-vib emission in the millimeter. This is currently set up to run on the short low modules of Spitzer IRS data (5.2 - 14.2 microns) but will be ideal for JWST/MIRI MRS data in the future.

[ascl:2211.003] AMBER: Abundance Matching Box for the Epoch of Reionization

AMBER (Abundance Matching Box for the Epoch of Reionization) models the cosmic dawn. The semi-numerical code allows users to directly specify the reionization history through the redshift midpoint, duration, and asymmetry input parameters. The reionization process is further controlled through the minimum halo mass for galaxy formation and the radiation mean free path for radiative transfer. The parallelized code is over four orders of magnitude faster than radiative transfer simulations and will efficiently enable large-volume models, full-sky mock observations, and parameter-space studies.

[ascl:2211.002] KC: Analytical propagator with collision detection for Keplerian systems

The analytic propagator Kepler-Collisions calculates collisions for Keplerian systems. The algorithm maintains a list of collision possibilities and jumps from one collision to the next; since collisions are rare in astronomical scales, jumping from collision to collision and calculating each one is more efficient than calculating all the time steps that are between collisions.

[ascl:2211.001] PTAfast: PTA correlations from stochastic gravitational wave background

PTAfast calculates the overlap reduction function in Pulsar Timing Array produced by the stochastic gravitational wave background for arbitrary polarizations, propagation velocities, and pulsar distances.

[ascl:2210.030] cuvarbase: fast period finding utilities for GPUs

cuvarbase provides a Python library for performing period finding (Lomb-Scargle, Phase Dispersion Minimization, Conditional Entropy, Box-least squares) on astronomical time-series datasets. Speedups over CPU implementations depend on the algorithm, dataset, and GPU capabilities but are typically ~1-2 orders of magnitude and are especially high for BLS and Lomb-Scargle.

[ascl:2210.029] paltas: Simulation-based inference on strong gravitational lensing systems

paltas conducts simulation-based inference on strong gravitational lensing images. It builds on lenstronomy (ascl:1804.012) to create large datasets of strong lensing images with realistic low-mass halos, Hubble Space Telescope (HST) observational effects, and galaxy light from HST's COSMOS field. paltas also includes the capability to easily train neural posterior estimators of the parameters of the lensing system and to run hierarchical inference on test populations.

[ascl:2210.028] CK: Cloud modeling and removal

Cloud Killer recovers surface albedo maps by using reflected light photometry to map the clouds and surface of unresolved exoplanets. For light curves with negligible photometric uncertainties, the minimal top-of-atmosphere albedo at a location is a good estimate of its surface albedo. On synthetic data, it shows little bias, good precision, and accuracy, but slightly underestimated uncertainties; exoplanets with large, changing cloud structures observed near quadrature phases are good candidates for Cloud Killer cloud removal.

[ascl:2210.027] LensingETC: Lensing Exposure Time Calculator

LensingETC optimizes observing strategies for multi-filter imaging campaigns of galaxy-scale strong lensing systems. It uses the lens modelling software lenstronomy (ascl:1804.012) to simulate and model mock imaging data, forecasts the lens model parameter uncertainties, and optimizes observing strategies.

[ascl:2210.026] PGOPHER: Rotational, vibrational, and electronic spectra simulator

PGOPHER simulates and fits rotational, vibrational, and electronic spectra. It handles linear molecules and symmetric and asymmetric tops, including effects due to unpaired electrons and nuclear spin, with a separate mode for vibrational structure. The code performs many sorts of transitions, including Raman, multiphoton, and forbidden transitions. It can simulate multiple species and states simultaneously, including special effects such as perturbations and state dependent predissociation. Fitting can be to line positions, intensities, or band contours. PGOPHER uses a standard graphical user interface and makes comparison with, and fitting to, spectra from various sources easy. In addition to overlaying numerical spectra, it is also possible to overlay pictures from pdf files and even plate spectra to assist in checking that published constants are being used correctly.

[ascl:2210.025] tvguide: Observability by TESS

tvguide determines whether stars and galaxies are observable by TESS. It uses an object's right ascension and declination and estimates the pointing of TESS's cameras using predicted spacecraft ephemerides to determine whether and for how long the object is observable with TESS. tvguide returns a file with two columns, the first the minimum number of sectors the target is observable for and the second the maximum.

[ascl:2210.024] Faiss: Similarity search and clustering of dense vectors library

The Faiss library performs efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU.

[ascl:2210.023] BornRaytrace: Weak gravitational lensing effects simulator

BornRaytrace uses neural data compression of weak lensing map summary statistics to simulate weak gravitational lensing effects. It can raytrace through overdensity Healpix maps to return a convergence map, include shear-kappa transformation on the full sphere, and also include intrinsic alignments (NLA model).

[ascl:2210.022] MCCD: Multi-CCD Point Spread Function Modelling

MCCD (Multi-CCD) generates a Point Spread Function (PSF) model based on stars observations in the field of view. After defining the MCCD model parameters and running and validating the training, the model can recover the PSF at any position in the field of view. Written in Python, MCCD also calculates various statistics and can plot a random test star and its model reconstruction.

[ascl:2210.021] SHEEP: Machine Learning pipeline for astronomy classification

The photometric redshift-aided classification pipeline SHEEP uses ensemble learning to classify astronomical sources into galaxies, quasars and stars. It uses tabular data and also allows the use of sparse data. The approach uses SDSS and WISE photometry, but SHEEP can also be used with other types of tabular data, such as radio fluxes or magnitudes.

[ascl:2210.020] ixpeobssim: Imaging X-ray Polarimetry Explorer simulator and analyzer

The simulation and analysis framework ixpeobssim was specifically developed for the Imaging X-ray Polarimetry Explorer (IXPE). It produces realistic simulated observations, in the form of event lists in FITS format, that also contain a strict superset of the information included in the publicly released IXPE data products. The framework's core simulation capabilities are complemented by post-processing applications that support the spatial, spectral, and temporal models needed for analysis of typical polarized X-ray sources, allowing implementation of complex, polarization-aware analysis pipelines. Where applicable, the data formats are consistent with the common display and analysis tools used by the community, e.g., the binned count spectra can be fed into XSPEC (ascl:9910.005), along with the corresponding response functions, for doing standard spectral analysis. All ixpeobssim simulation and analysis tools are fully configurable via the command line.

[ascl:2210.019] POSYDON: Single and binary star population synthesis code

POSYDON (POpulation SYnthesis with Detailed binary-evolution simulatiONs) incorporates full stellar structure and evolution modeling for single and binary-star population synthesis. The code is modular and allows the user to specify initial population properties and adopt choices that determine how stellar evolution proceeds. Populations are simulated with the use of MESA (ascl:1010.083) evolutionary tracks for single, non-interacting, and interacting binaries organized in grids. Machine-learning methods are incorporated and applied on the grids for classification and various interpolation calculations, and the development of irregular grids guided by active learning, for computational efficiency.

[ascl:2210.018] LavAtmos: Gas-melt equilibrium calculations for a given temperature and melt composition

LavAtmos performs gas-melt equilibrium calculations for a given temperature and melt composition. The thermodynamics of the melt are modeled by the MELTS code as presented in the Thermoengine package (ascl:2208.006). In combination with atmospheric chemistry codes, LavAtmos enables the characterization of interior compositions through atmospheric signatures.

[ascl:2210.017] PySME: Spectroscopy Made Easy reimplemented with Python

PySME is a partial reimplementation of Spectroscopy Made Easy (SME, ascl:1202.013), which fits an observed spectrum of a star with a model spectrum. The IDL routines of SME used to call a dynamically linked library of compiled C++ and Fortran programs have been rewritten in Python. In addition, an object oriented paradigm and continuous integration practices, including build automation, self-testing, and frequent builds, have been added.

[ascl:2210.016] PETSc: Portable, Extensible Toolkit for Scientific Computation

PETSc (Portable, Extensible Toolkit for Scientific Computation) provides a suite of data structures and routines for the scalable (parallel) solution of scientific applications modeled by partial differential equations, and is intended for use in large-scale application projects. The toolkit includes a large suite of parallel linear, nonlinear equation solvers and ODE integrators that are easily used in application codes written in C, C++, Fortran and Python. PETSc provides many of the mechanisms needed within parallel application codes, such as simple parallel matrix and vector assembly routines that allow the overlap of communication and computation. In addition, PETSc (pronounced PET-see) includes support for managing parallel PDE discretizations.

[ascl:2210.015] Solar-MACH: Multi-spacecraft longitudinal configuration plotter

Solar-MACH (Solar MAgnetic Connection HAUS) derives and visualizes the spatial configuration and solar magnetic connection of different observers (i.e., spacecraft or planets) in the heliosphere at different times. It provides publication-ready figures for analyzing Solar Energetic Particle events (SEPs) or solar transients such as Coronal Mass Ejections (CMEs). Solar-MACH is available as a Python package; a Streamlit-enabled tool that runs in a browser is also available (solar-mach.github.io)

[ascl:2210.014] Blacklight: GR ray tracing code for post-processing Athena++ simulations

Blacklight postprocesses general-relativistic magnetohydrodynamic simulation data and produces outputs for analyzing data sets, including maps of auxiliary quantities and false-color renderings. The code can use Athena++ (ascl:1912.005) outputs directly, and also supports files in HARM (ascl:1209.005) and iHARM3d (ascl:2210.013) format. Written in C++, Blacklight offers support for adaptive mesh refinement input, slow-light calculations, and adaptive ray tracing.

[ascl:2210.013] iharm3D: Hybrid MPI/OpenMP 3D HARM with vectorization

iharm3D implements the HARM algorithm (ascl:1209.005) with modifications and enables a second-order, conservative, shock-capturing scheme for general-relativistic magnetohydrodynamics (GRMHD). Written in C, it simulates black hole accretion systems in arbitrary stationary spacetimes.

[ascl:2210.012] pixmappy: Python interface to gbdes astrometry solutions

pixmappy provides a Python interface to gbdes pixel map (astrometry) solutions. It reads the YAML format astrometry solutions produced by gbdes (ascl:2210.011) and issues a PixelMap instance, which is a map from one 2d coordinate system ("pixel") to another ("world") 2d system. A PixelMap instance can be used as a function mapping one (or many) coordinate pairs. An inverse method does reverse mapping, and the local jacobian of the map is available also. The type of mapping that can be expressed is very flexible, and PixelMaps can be compounded into chains of tranformations.

[ascl:2210.011] gbdes: DECam instrumental signature fitting and processing programs

gbdes derives photometric and astrometric calibration solutions for complex multi-detector astronomical imagers. The package includes routines to filter catalogs down to useful stellar objects, collect metadata from the catalogs and create a config file holding FITS binary tables describing exposures, instruments, fields, and other available information in the data, and uses a friends-of-friends matching algorithm to link together all detections of common objects found in distinct exposures. gbdes also calculates airmasses and parallactic angles for each exposure, calculates and saves the expected differential chromatic refraction (DCR) needed for precision astrometry, optimizes the parameters of a photometric model to maximize agreement between magnitudes measured in different exposures of the same source, and optimizing the parameters of an astrometric model to maximize agreement among the exposures and any reference catalogs, and performs other tasks. The solutions derived and used by gbdes are stored in YAML format; gbdes uses the Python code pixmappy (ascl:2210.012) to read the astrometric solution files and execute specified transformations.

[ascl:2210.010] TSRecon: Time series reconstruction method of massive astronomical catalogs

The time series reconstruction method of massive astronomical catalogs reconstructs all celestial objects' time series data for astronomical catalogs with great accuracy. In addition, the program, which requires a Spark cluster, solves the boundary source leakage problem on the premise of ensuring accuracy, and the user can set different parameters for different data sets to filter the error records in the catalogs.

[ascl:2210.009] NEMESIS: Non-linear optimal estimator for multivariate spectral analysis

NEMESIS (Non-linear optimal Estimator for MultivariatE spectral analySIS) is the general purpose correlated-k/LBL retrieval code developed from the RADTRAN project (ascl:2210.008). Originally based on the correlated-k approximation, NEMESIS also works in line-by-line (LBL) mode. It has been designed to be generally applicable to any planet and with any observing mode and so is suitable for both solar-system studies and also exoplanetary studies.

[ascl:2210.008] RADTRAN: General purpose planetary radiative transfer model

RADTRAN calculates the transmission, absorption or emission spectra emitted by planetary atmospheres using either line-by-line integration, spectral band models, or 'correlated-K' approaches. Part of the NEMESIS project (ascl:2210.009), the code also incorporates both multiple scattering and single scattering calculations. RADTRAN is general purpose and not hard-wired to any specific planet.

[ascl:2210.007] COMET: Emulated predictions of large-scale structure observables

COMET (Clustering Observables Modelled by Emulated perturbation Theory) provides emulated predictions of large-scale structure observables from models that are based on perturbation theory. It substantially speeds up these analytic computations without any relevant sacrifice in accuracy, enabling an extremely efficient exploration of large-scale structure likelihoods. At its core, COMET exploits an evolution mapping approach which gives it a high degree of flexibility and allows it to cover a wide cosmology parameter space at continuous redshifts up to z∼3z \sim 3z∼3. Among others, COMET supports parameters for cold dark matter density (ωc\omega_cωc​), baryon density (ωb\omega_bωb​), Scalar spectral index (nsn_sns​), Hubble expansion rate (hhh) and Curvature density (ΩK\Omega_KΩK​). The code can obtain the real-space galaxy power spectrum at one-loop order multipoles (monopole, quadrupole, hexadecapole) of the redshift-space, power spectrum at one-loop order, the linear matter power spectrum (with and without infrared resummation), Gaussian covariance matrices for the real-space power spectrum, and redshift-space multipoles and χ2\chi^2χ2's for arbitrary combinations of multipoles. COMET provides an easy-to-use interface for all of these computations.

[ascl:2210.006] ExoRad2: Generic point source radiometric model

ExoRad 2.0, a generic point source radiometric model, interfaces with any instrument to provide an estimate of several Payload performance metrics. For each target and for each photometric and spectroscopic channel, the code provides estimates of signals in pixels, saturation times, and read, photon, and dark current noise. ExoRad also provides estimates for the zodiacal background, inner sanctum, and sky foreground.

[ascl:2210.005] PSFr: Point Spread Function reconstruction

PSFr empirically reconstructs an oversampled version of the point spread function (PSF) from astronomical imaging observations. The code provides a light-weighted API of a refined version of an algorithm originally implemented in lenstronomy (ascl:1804.012). It provides user support with different artifacts in the data and supports the masking of pixels, or the treatment of saturation levels. PSFr has been used to reconstruct the PSF from multiply imaged lensed quasar images observed by the Hubble Space Telescope in a crowded lensing environment and more recently with James Webb Space Telescope (JWST) imaging data for a wide dynamical flux range.

[ascl:2210.004] Finder_charts: Create finder charts from image data of various sky surveys

Finder_charts creates multi-band finder charts from image data of various partial- and all-sky surveys such as DSS, 2MASS, WISE, UKIDSS, VHS, Pan-STARRS, and DES. It also creates a WISE time series of image data acquired between 2010 and 2021. All images are reprojected so that north is up and east is to the left. The resulting finder charts can be overplotted with corresponding catalog positions. All catalog entries within the specified field of view can be saved in a variety of formats, including ipac, csv, and tex, as can the finder charts in png, pdf, eps, and other common graphics formats. Finder_charts consists of a single Python module, which depends only on well-known packages, making it easy to install.

[ascl:2210.003] NIRDust: Near Infrared Dust finder for Type2 AGN K-band spectra

NIRDust uses K-band (2.2 micrometers) spectra to measure the temperature of the dust heated by an Active Galactic Nuclei (AGN) accretion disk. The package provides several functionalities to pre-process spectra and fit the hot dust component of a AGN K-band spectrum with a blackbody function. NIRDust needs a minimum of two spectra to run: a target spectrum, where the dust temperature will be estimated, and a reference spectrum, where the emission is considered to be purely stellar. The reference spectrum will be used by NIRDust to model the stellar emission from the target spectrum.

[ascl:2210.002] SPINspiral: Parameter estimation for analyzing gravitational-wave signals

SPINspiral analyzes gravitational-wave signals from stellar-mass binary inspirals detected by ground-based interferometers such as LIGO and Virgo. It performs parameter estimation on these signals using Markov-chain Monte-Carlo (MCMC) techniques. This analysis includes the spins of the binary components. Written in C, the package is modular; its main routine is as small as possible and calls other routines, which perform tasks such as reading input, choosing and setting (starting or injection) parameters, and handling noise. Other routines compute overlaps and likelihoods, contain the MCMC core, and manage more general support functions and third-party routines.

[ascl:2210.001] PSS: Pulsar Survey Scraper

Pulsar Survey Scraper aggregates pulsar discoveries before they are included in the ATNF pulsar catalog and enables searching and filtering based on position and dispersion measure. This facilitates identifying new pulsar discoveries. Pulsar Survey Scraper can be downloaded or run online using the Pulsar Survey Scraper webform.

[submitted] EleFits

EleFits is a modern C++ package to read and write FITS files which focuses on safety, user-friendliness, and performance.

[ascl:2209.020] FastQSL: Quasi-separatrix Layers computation method

FastQSL calculate the squashing factor Q at the photosphere, a cross section, or a box volume, given a 3D magnetic field with Cartesian, uniform or stretched grids. It is available in IDL and in an optimized version using Fortran for calculations and field line tracing. Use of a GPU accelerates a step-size adaptive scheme for the most computationally intensive part, the field line tracing, making the code fast and efficient.

[ascl:2209.019] SolTrack: Compute the position of the Sun in topocentric coordinates

SolTrack computes the position of the Sun, the rise and set times and azimuths, and transit times and altitudes. It includes corrections for aberration and parallax, and has a simple routine to correct for atmospheric refraction, taking into account local atmospheric conditions. SolTrack is derived from the Fortran library libTheSky (ascl:2209.018). The package can be used to track the Sun on a low-specs machine, such as a microcontroller or PLC, and can be used for (highly) concentrated (photovoltaic) solar power or accurate solar-energy modeling.

[ascl:2209.018] libTheSky: Compute positions of celestial bodies and events

libTheSky compute the positions of celestial bodies, such as the Moon, planets, and stars, and events, including conjunctions and eclipses, with great accuracy. Written in Fortran, libTheSky can use different reference frames (heliocentric, geocentric, topocentric) and coordinate systems (ecliptic, equatorial, galactic; spherical, rectangular), and the user can choose low- or high-accuracy calculations, depending on need.

[ascl:2209.017] SpectraPy: Extract and reduce astronomical spectral data

SpectraPy collects algorithms and methods for data reduction of astronomical spectra obtained by a through slits spectrograph. It produces two-dimensional wavelength calibrated spectra corrected by instrument distortions. The library is designed to be spectrograph independent and can be used on both longslit (LS) and multi object spectrograph (MOS) data. SpectraPy comes with a set of already configured spectrographs, but it can be easily configured to reduce data of other instruments.

[ascl:2209.016] RAPOC: Rosseland and Planck mean opacities calculator

RAPOC (Rosseland and Planck Opacity Converter) uses molecular absorption measurements (i.e., wavelength-dependent opacities) for a given temperature, pressure, and wavelength range to calculate Rosseland and Planck mean opacities for use in atmospheric modeling. The code interpolates between discrete data points and can use ExoMol and DACE data, or any user-defined data provided in a readable format. RAPOC is simple, straightforward, and easily incorporated into other codes.

[ascl:2209.015] TauREx3: Tau Retrieval for Exoplanets

TauREx 3 (Tau Retrieval for Exoplanets) provides a fully Bayesian inverse atmospheric retrieval framework for exoplanetary atmosphere modeling and retrievals. It is fully customizable, allowing the user to mix and match atmospheric parameters and add additional ones. The framework builds forward models, simulates instruments, and performs retrievals, and provides a rich library of classes for building additional programs and using new atmospheric parameters.

[ascl:2209.014] SyntheticISOs: Synthetic Population of Interstellar Objects

Synthetic Population of Interstellar Objects generates a synthetic population of interstellar objects (orbits and sizes) in arbitrary volume of space around the Sun. The only necessary assumption is that the population of ISOs in the interstellar space (far from any massive body) is homogeneous and isotropic. The assumed distribution of interstellar velocities of ISOs has to be provided as an input. This distribution can be defined analytically, but also in a discrete form. The algorithm, based on the multivariate inverse transform sampling method, is implemented in Python.

[ascl:2209.013] wsynphot: Synthetic photometry package using quantities

wsynphot provides a broad set of filters, including observation facility, instrument, and wavelength range, and functions for imaging stars to produce a filter curve showing the transmission of light for each wavelength value. It can create a filter curve object, plot the curve, and allows the user to do calculations on the filter curve object.

[ascl:2209.012] URILIGHT: Time-dependent Monte-Carlo radiative-transfer

The time dependent Monte-Carlo code URILIGHT, written in Fortran 90, assumes homologous expansion. Energy deposition resulting from the decay of radioactive isotopes is calculated by a Monte-Carlo solution of the γ-ray transport, for which interaction with matter is included through Compton scattering and photoelectric absorption. The temperature is iteratively solved for in each cell by requiring that the total emissivity equals the total absorbed energy.

[ascl:2209.011] GaLight: 2D modeling of galaxy images

GaLight (Galaxy shapes of Light) performs two-dimensional model fitting of optical and near-infrared images to characterize the light distribution of galaxies with components including a disk, bulge, bar and quasar. Light is decomposes into PSF and Sersic, and the fitting is based on lenstronomy (ascl:1804.01). GaLight's automated features including searching PSF stars in the FOV, automatically estimating the background noise level, and cutting out the target object galaxies (QSOs) and preparing the materials to model the data. It can also detect objects in the cutout stamp and quickly create Sersic keywords to model them, and model QSOs and galaxies using 2D Sersic profile and scaled point source.

[ascl:2209.010] HyPhy: Hydrodynamical Physics via Deep Generative Painting

HyPhy maps from dark matter only simulations to full hydrodynamical physics models. It uses a fully convolutional variational auto-encoder (VAE) to synthesize hydrodynamic fields conditioned on dark matter fields from N-body simulations. After training, HyPhy can probabilistically map new dark matter only simulations to corresponding full hydrodynamical outputs and generate posterior samples for studying the variance of the mapping. This conditional deep generative model is implemented in TensorFlow.

[ascl:2209.009] GRUMPY: Galaxy formation with RegUlator Model in PYthon

GRUMPY (Galaxy formation with RegUlator Model in PYthon) models the formation of dwarf galaxies. When coupled with realistic mass accretion histories of halos from simulations and reasonable choices for model parameter values, this simple regulator-type framework reproduces a broad range of observed properties of dwarf galaxies over seven orders of magnitude in stellar mass. GRUMPY matches observational constraints on the stellar mass--halo mass relation and observed relations between stellar mass and gas phase and stellar metallicities, gas mass, size, and star formation rate. It also models the general form and diversity of star formation histories (SFHs) of observed dwarf galaxies. The software can be used to predict photometric properties of dwarf galaxies hosted by dark matter haloes in N-body simulations, such as colors, surface brightnesses, and mass-to-light ratios and to forward model observations of dwarf galaxies.

[ascl:2209.008] PINION: Accelerating radiative transfer simulations for cosmic reionization

PINION (Physics-Informed neural Network for reIONization) predicts the complete 4-D hydrogen fraction evolution from the smoothed gas and mass density fields from pre-computed N-body simulations. Trained on C2-Ray simulation outputs with a physics constraint on the reionization chemistry equation, PINION accurately predicts the entire reionization history between z = 6 and 12 with only five redshift snapshots and a propagation mask as a simplistic approximation of the ionizing photon mean free path. The network's predictions are in good agreement with simulation to redshift z > 7, though the oversimplified propagation mask degrades the network's accuracy for z < 7.

[ascl:2209.007] AMBER: Fast pipeline for detecting single-pulse radio transients

AMBER (Apertif Monitor for Bursts Encountered in Real-time) detects single-pulse radio phenomena, such as pulsars and fast radio bursts, in real time. It is a fully auto-tuned pipeline that offloads compute-intensive kernels to many-core accelerators; the software automatically tunes these kernels to achieve high performance on different platforms.

[ascl:2209.006] KaRMMa: Curved-sky mass map reconstruction

KaRMMa (Kappa Reconstruction for Mass MApping) performs curved-sky mass map reconstruction using a lognormal prior from weak-lensing surveys. It uses a fully Bayesian approach with a physically motivated lognormal prior to sample from the posterior distribution of convergence maps. The posterior distribution of KaRMMa maps are nearly unbiased in one-point and two-point functions and peak/void counts. KaRMMa successfully captures the non-Gaussian nature of the distribution of κ values in the simulated maps, and KaRMMa posteriors correctly characterize the uncertainty in summary statistics.

[ascl:2209.005] SCORE: Shape COnstraint REstoration

The Shape COnstraint REstoration algorithm (SCORE) is a proximal algorithm based on sparsity and shape constraints to restore images. Its main purpose is to restore images while preserving their shape information. It can, for example, denoise a galaxy image by instanciating SCORE and using its denoise method and then visualize the results, and can deconvolve multiple images with different parameter values.

[ascl:2209.004] Cluster Toolkit: Tools for analyzing galaxy clusters

Cluster Toolkit calculates weak lensing signals from galaxy clusters and cluster cosmology. It offers 3D density and correlation functions, halo bias models, projected density and differential profiles, and radially averaged profiles. It also calculates halo mass functions, mass-concentration relations, Sunyaev-Zel’dovich (SZ) cluster signals, and cluster magnification. Cluster Toolkit consists of a Python front end wrapped around a well optimized back end in C.

[ascl:2209.003] DeepMass: Cosmological map inference with deep learning

DeepMass infers dark matter maps from weak gravitational lensing measurements and uses deep learning to reconstruct cosmological maps. The code can also be incorporated into a Moment Network to enable high-dimensional likelihood-free inference.

[ascl:2209.002] Herculens: Differentiable gravitational lensing

Herculens models imaging data of strong gravitational lenses. The package supports various degrees of model complexity, ranging from standard smooth analytical profiles to pixelated models and machine learning approaches. In particular, it implements multiscale pixelated models regularized with sparsity constraints and wavelet decomposition, for modeling both the source light distribution and the lens potential. The code is fully differentiable - based on JAX (ascl:2111.002) - which enables fast convergence to the solution, access to the parameters covariance matrix, efficient exploration of the parameter space including the sampling of posterior distributions using variational inference or Hamiltonian Monte-Carlo methods.

[ascl:2209.001] A-SLOTH: Semi-analytical model to connect first stars and galaxies to observables

A-SLOTH (Ancient Stars and Local Observables by Tracing Halos) connects the formation of the first stars and galaxies to observables. The model is based on dark matter merger trees, on which A-SLOTH applies analytical recipes for baryonic physics to model the formation of both metal-free and metal-poor stars and the transition between them. The software samples individual stars and includes radiative, chemical, and mechanical feedback. A-SLOTH has versatile applications with moderate computational requirements. It can be used to constrain the properties of the first stars and high-z galaxies based on local observables, predicts properties of the oldest and most metal-poor stars in the Milky Way, can serve as a subgrid model for larger cosmological simulations, and predicts next-generation observables of the early Universe, such as supernova rates or gravitational wave events.

[ascl:2208.025] Yonder: Data denoising and reconstruction

YONDER uses singular value decomposition to perform low-rank data denoising and reconstruction. It takes a tabular data matrix and an error matrix as input and returns a denoised version of the original dataset as output. The approach enables a more accurate data analysis in the presence of uncertainties. Consequently, this package can be used as a simple toolbox to perform astronomical data cleaning.

[ascl:2208.024] toise: Performance estimator for high-energy neutrino detectors

The toise framework estimates the sensitivity of natural-medium neutrino detectors such as IceCube-Gen2 to sources of high-energy astrophysical neutrinos. It uses parameterizations of a detector's fiducial area or volume, selection efficiency, energy resolution, angular resolution, and event classification efficiency to convert (surface) neutrino fluxes into mean event rates in bins of observable space. These are then used to estimate statistical quantities of interest, e.g., the median sensitivity to some flux (i.e., 90% upper limit assuming the true flux is zero) or the median discovery potential (i.e., the flux level at which the null hypothesis would be rejected at 5 sigma in 50% of realizations).

[ascl:2208.023] CubeFit: Regularized 3D fitting for spectro-imaging data

Cubefit is an OXY class that performs spectral fitting with spatial regularization in a spectro-imaging context. The 3D model is based on a 1D model and 2D parameter maps; the 2D maps are regularized using an L1L2 regularization by default. The estimator is a compound of a chi^2 based on the 1D model, a regularization term based of the 2D regularization of the various 2D parameter maps, and an optional decorrelation term based on the cross-correlation of specific pairs of parameter maps.

[ascl:2208.022] PyNAPLE: Automated pipeline for detecting changes on the lunar surface

PyNAPLE (PYthon Nac Automated Pair Lunar Evaluator) detects changes and new impact craters on the lunar surface using Lunar Reconnaissance Orbiter Narrow Angle Camera (LRO NAC) images. The code enables large scale analyses of sub-kilometer scale cratering rates and refinement of both scaling laws and the luminous efficiency.

[ascl:2208.021] GSSP: Grid Search in Stellar Parameters

GSSP (Grid Search in Stellar Parameters) is based on a grid search in the fundamental atmospheric parameters and (optionally) individual chemical abundances of the star (or binary stellar components) in question. It uses atmosphere models and spectrum synthesis, which assumes a comparison of the observations with each theoretical spectrum from the grid. The code can optimize five stellar parameters at a time (effective temperature, surface gravity, metallicity, microturbulent velocity, and projected rotational velocity of the star) and synthetic spectra can be computed in any number of wavelength ranges. GSSP builds the grid of theoretical spectra from all possible combinations of the above mentioned parameters, and delivers the set of best fit parameters, the corresponding synthetic spectrum, and the ASCII file containing the individual parameter values for all grid points and the corresponding chi-square values.

[ascl:2208.020] GStokes: Magnetic field structure and line profiles calculator

GStokes performs simple multipolar fits to circular polarization data to provide information about the field strength and geometry. It provides forward calculation of the disc-integrated Stokes parameter profiles as well as magnetic inversions under several widely used simplifying approximations of the polarized line formation. GStokes implements the Unno–Rachkovsky analytical solution of the polarized radiative transfer equation and the weak-field approximation with the Gaussian local profiles. The magnetic field geometry is described with one of the common low-order multipolar field parametrizations. Written in IDL, GStokes provides a user-friendly graphical front-end.

[ascl:2208.019] RadioLensfit: Radio weak lensing shear measurement in the visibility domain

RadioLensfit measures star-forming galaxy ellipticities using a Bayesian model fitting approach. The software uses an analytical exponential Sersic model and works in the visibility domain avoiding Fourier Transform. It also simulates visibilities of observed SF galaxies given a source catalog and Measurement Sets containing the description of the radio interferometer and of the observation. It provides both serial and MPI versions.

[ascl:2208.018] EstrellaNueva: Expected rates of supernova neutrinos calculator

EstrellaNueva calculates expected rates of supernova neutrinos in detectors. It provides a link between supernova simulations and the expected events in detectors by calculating fluences and event rates in order to ease any comparison between theory and observation. The software is a standalone tool for exploring many physics scenarios, and offers an option to add analytical cross sections and define any target material.

[ascl:2208.017] HOCHUNK3D: Dust radiative transfer in 3D

HOCHUNK3D is an updated version of the HOCHUNK radiative equilibrium code (ascl:1711.013); the code has been converted to Fortran 95, which allows a specification of one-dimensional (1D), 2D, or 3D grids at runtime. The code is parallelized so it can be run on multiple processors on one machine, or on multiple machines in a network. It includes 3-D functionality and several other additional geometries and features. The code calculates radiative equilibrium temperature solution, thermal and PAH/vsg emission, scattering and polarization in protostellar geometries. HOCHUNK3D also computes spectral energy distributions (SEDs), polarization spectra, and images.

[ascl:2208.016] CRPropa3: Simulation framework for propagating extraterrestrial ultra-high energy particles

CRPropa3, an improved version of CRPropa2 (ascl:1412.013), provides a simulation framework to study the propagation of ultra-high-energy nuclei up to iron on their voyage through an (extra)galactic environment. It takes into account pion production, photodisintegration, and energy losses by pair production of all relevant isotopes in the ambient low-energy photon fields, as well as nuclear decay. CRPropa3 can model the deflection in (inter)galactic magnetic fields, the propagation of secondary electromagnetic cascades, and neutrinos for a multitude of scenarios for different source distributions and magnetic environments. It enables the user to predict the spectra of UHECR (and of their secondaries), their composition and arrival direction distribution. Additionally, the low-energy Galactic propagation can be simulated by solving the transport equation using stochastic differential equations. CRPropa3 features a very flexible simulation setup with python steering and shared-memory parallelization.

[ascl:2208.015] J-comb: Combine high-resolution and low-resolution data

J-comb combines high-resolution data with large-scale missing information with low-resolution data containing the short spacing. Based on uvcombine (ascl:2208.014), it takes as input FITS files of low- and high-resolution images, the angular resolution of the input images, and the pixel size of the input images, and outputs a FITS file of the combined image.

[ascl:2208.014] uvcombine: Combine images with different resolutions

uvcombine combines single-dish and interferometric data. It can combine high-resolution images that are missing large angular scales (Fourier-domain short-spacings) with low-resolution images containing the short/zero spacing. uvcombine includes the "feathering" technique for interferometry data, implementing a similar approach to CASA’s (ascl:1107.013) feather task but with additional options. Also included are consistency tests for the flux calibration and single-dish scale by comparing the data in the uv-overlap range.

[ascl:2208.013] SPAMMS: Spectroscopic PAtch Model for Massive Stars

SPAMMS (Spectroscopic PAtch Model for Massive Stars), designed with geometrically deformed systems in mind, combines the eclipsing binary modelling code PHOEBE 2 (ascl:1106.002) and the NLTE radiative transfer code FASTWIND to produce synthetic spectra for systems at given phases, orientations and geometries. SPAMMS reproduces the morphology of observed spectral line profiles for overcontact systems and the Rossiter-Mclaughlin and Struve-Sahade effects.

[ascl:2208.012] DELIGHT: Identify host galaxies of transient candidates

DELIGHT (Deep Learning Identification of Galaxy Hosts of Transients) automatically identifies host galaxies of transient candidates using multi-resolution images and a convolutional neural network. This library has a class with several methods to get the most likely host coordinates starting from given transient coordinates. In order to do this, the DELIGHT object needs a list of object identifiers and coordinates (oid, ra, dec). With this information, it downloads PanSTARRS images centered around the position of the transients (2 arcmin x 2 arcmin), gets their WCS solutions, creates the multi-resolution images, does some extra preprocessing of the data, and finally predicts the position of the hosts using a multi-resolution image and a convolutional neural network. DELIGHT can also estimate the host's semi-major axis if requested, taking advantage of the multi-resolution images.

[ascl:2208.011] POIS: Python Optical Interferometry Simulation

POIS (Python Optical Interferometry Simulation) provides the building blocks to simulate the operation of a ground-based optical interferometer perturbed by atmospheric seeing perturbations. The package includes functions to generate simulated atmospheric turbulent wavefront perturbations, correct these perturbations using adaptive optics, and combine beams from an arbitrary number of telescopes, with or without spatial filtering, to provide complex fringe visibility measurements.

[ascl:2208.010] FFD: Flare Frequency Distribution

FFD (Flare Frequency Distribution) fits power-laws to FFDs. FFDs relate the frequency (i.e., occurrence rate) of flares to their energy, peak flux, photometric equivalent width, or other parameters. This module was created to handle disparate datasets between which the flare detection limit varies; in essence, the number of flares detected is treated as following a Poisson distribution while the flare energies are treated as following a power law.

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