nessai performs nested sampling for Bayesian Inference and incorporates normalizing flows. It is designed for applications where the Bayesian likelihood is computationally expensive. nessai uses PyTorch and also supports the use of bilby (ascl:1901.011).

raynest, written in Python, computes Bayesian evidences and probability distributions using parallel chains.

pyADfit models accretion discs around astrophysical objects. The code provides functions to calculate physical quantities related to accretion disks and perform parameter estimation using observational data. The accretion disc model is the alpha-disc model while the parameter estimation can be performed with Nessai (ascl:2405.002), Raynest (ascl:2405.003), or CPnest (ascl:2205.021).

pySPEDAS (Python-based Space Physics Environment Data Analysis Software) supports multi-mission, multi-instrument retrieval, analysis, and visualization of heliophysics time series data. A Python implementation of SPEDAS (ascl:2405.001), it supports most of the capabilities of SPEDAS; it can load heliophysics data sets from more than 30 space-based and ground-based missions, coordinate transforms, interpolation routines, and unit conversions, and provide interactive access to numerous data sets. pySPEDAS also creates multi-mission, multi-instrument figures, includes field and wave analysis tools, and performs magnetic field modeling, among other functions.

ICPertFLRW, a Cactus code (ascl:1102.013) thorn, provides as initial conditions an FLRW metric perturbed with the comoving curvature perturbation Rc in the synchronous comoving gauge. Rc is defined as a sum of sinusoidals (20 in each x, y, and z direction) whose amplitude, wavelength, and phase shift are all parameters in param.ccl. While the metric and extrinsic curvature only have first order scalar perturbations, the energy density is computed exactly in full from the Hamiltonian constraint, hence vector and tensor perturbations are initially present at higher order. These are then passed to the CT_Dust thorn to be evolved.

GauPro fits a Gaussian process regression model to a dataset. A Gaussian process (GP) is a commonly used model in computer simulation. It assumes that the distribution of any set of points is multivariate normal. A major benefit of GP models is that they provide uncertainty estimates along with their predictions.

i-SPin simulates 3-component Schrodinger systems with and without gravity and with and without self-interactions while obeying SO(3) symmetry. The code allows the user to input desired parameters, along with initial conditions for the Schrodinger fields. Its three function modules then perform the main (drift-kick-drift) steps of the algorithm, track the fractional changes in total mass and spin in the system, and then plot results. The default plots are mass and spin density projections along with total mass and spin fractional changes.

morphen performs image analysis, multi-Sersic image fitting decomposition, and radio interferometric self-calibration, thus measuring basic image morphology and photometry. The code provides a state-of-the-art Python-based image fitting implementation based on the Sersic function. Geared, though not exclusively, toward radio astronomy, morphen's tools involve pure python, but also are integrated with CASA (ascl:1107.013) in order to work with common casatasks as well as WSClean (ascl:1408.023).

riddler automates fitting of type Ia supernovae spectral time series. The code is comprised of a series of neural networks trained to emulate radiative transfer simulations from TARDIS (ascl:1402.018). Emulated spectra are then fit to observations using nested sampling implemented in UltraNest (ascl:1611.001) to estimate the posterior distributions of model parameters and evidences.

MMLPhoto-z is a cross-modal contrastive learning approach for estimating photo-z of quasars. This method employs adversarial training and contrastive loss functions to promote the mutual conversion between multi-band photometric data features (magnitude, color) and photometric image features, while extracting modality-invariant features.

DirectSHT performs direct spherical harmonic transforms for point sets on the sphere. Given a set of points, defined by arrays of theta and phi (in radians) and weights, it provides the spherical harmonic transform coefficients alm. JAX (ascl:2111.002) can be used to speed up the computation; the code will automatically fall back to numpy if JAX is not present. The code is much faster when run on GPUs. When they are available and JAX is installed, the code automatically distributes computation and memory across them.

fitramp fits a ramp to a series of nondestructive reads and detects and rejects jumps. The software performs likelihood-based jump detection for detectors read out up-the-ramp; it uses the entire set of reads to compute likelihoods. The code compares the χ² value of a fit with and without a jump for every possible jump location. fitramp can fit ramps with and without fitting the reset value (the pedestal), and fit and mask jumps within or between groups of reads. It can also compute the bias of ramp fitting.

LTdwarfIndices studies spectral indices to determine whether one or more brown dwarfs are photometric variable candidates. For a single brown dwarf, it analyzes a given set of indices and outputs the number of graphs the object appears in in the variable area, whether it is a variable or non-variable candidate, and, optionally, an index-index or histogram plot. Using another code module, LTdwarftIndices can also analyze a set of sample indices for many brown dwarfs.

EF-TIGRE (Effective Field Theory of Interacting dark energy with Gravitational REdshift) constrains interacting Dark Energy/Dark Matter models in the Effective Field Theory framework through Large Scale Structures observables. In particular, the observables include the effect of gravitational redshift, a distortion of time from galaxy clustering. This generates a dipole in the correlation function which is detectable with two distinct populations of galaxies, thus making it possible to break degeneracies among parameters of the EFT description.

sunbather simulates the upper atmospheres of exoplanets and their observational signatures. The code constructs 1D Parker wind profiles using p-winds (ascl:2111.011) to simulate these with Cloudy (ascl:9910.001), and postprocesses the output with a custom radiative transfer module to predict the transmission spectra of exoplanets.

autoregressive-bbh-inference, written in Python, models the distributions of binary black hole masses, spins, and redshifts to identify physical features appearing in these distributions without the need for strongly-parametrized population models. This allows not only agnostic study of the “known unknowns” of the black hole population but also reveals the “unknown unknowns," the unexpected and impactful features that may otherwise be missed by the standard building-block method.

AFINO (Automated Flare Inference of Oscillations) finds oscillations in time series data using a Fourier-based model comparison approach. The code analyzes the date and generates a results file in either JSON or Pickle format, which contains numerous properties of the data and analysis, and a summary plot.

coronagraph_noise simulates coronagraph noise. Written in IDL, the code includes a generalized coronagraph routine and simulators for the WFIRST Shaped Pupil Coronagraph in both spectroscopy and imaging modes. Functions available include stellar and planetary flux functions, planet photon and zodiacal light count rates, planet-star flux ratio, and clock induced charge count rate, among others. coronagraph_noise also includes routines to smooth a plot by convolving with a Gaussian profile to convolve a spectrum with a given instrument resolution and to take a spectrum that is specified at high spectral resolution and degrade it to a lower resolution. A Python implementation of coronagraph_noise, coronagraph (ascl:2405.019), is also available.

coronagraph provides a Python noise model for directly imaging exoplanets with a coronagraph-equipped telescope. Based on the original IDL code for this coronagraph model, coronograph_noise (ascl:2405.018), the Python version has been expanded in a few key ways. Most notably, the Telescope, Planet, and Star objects used for reflected light coronagraph noise modeling can now be used for transmission and emission spectroscopy noise modeling, making this model a general purpose exoplanet noise model for many different types of observations.

tapify implements a suite of multitaper spectral estimation techniques for analyzing time series data. It supports analysis of both evenly and unevenly sampled time series data. The multitaper statistic tackles the problems of bias and consistency, which makes it an improvement over the classical periodogram for evenly sampled data and the Lomb-Scargle periodogram for uneven sampling. In basic statistical terms, this estimator provides a confident look at the properties of a time series in the frequency or Fourier domain.

PALpy provides a Python interface to PAL, the positional Astronomy Library (ascl:1606.002), which is written in C. All arguments modified by the C API are returned and none are modified. The one routine that is different is palObs, which returns a simple dict that can be searched using standard Python. The keys to the dict are the short names and the values are another dict with keys name, long, lat and height.

blackthorn generates spectra of dark matter annihilations into right-handed (RH) neutrinos or into particles that result from their decay. These spectra include photons, positrons, and neutrinos. The code provides support for varied RH-neutrino masses ranging from MeV to TeV by incorporating hazma, PPPC4DMID, and HDMSpectra models to compute dark matter annihilation cross sections and mediator decay widths. blackthorn also computes decay branching fractions and partial decay widths.

raccoon implements the cross-correlation function (CCF) method. It builds weighted binary masks from a stellar spectrum template, computes the CCF of stellar spectra with a mask, and derives radial velocities (RVs) and activity indicators from the CCF. raccoon is mainly implemented in Python 3; it also uses some Fortran subroutines that are called from Python.

ndcube manipulates, inspects, and visualizes multi-dimensional contiguous and non-contiguous coordinate-aware data arrays. A sunpy (ascl:1401.010) affiliated package, it combines data, uncertainties, units, metadata, masking, and coordinate transformations into classes with unified slicing and generic coordinate transformations and plotting and animation capabilities. ndcube handles data of any number of dimensions and axis types (e.g., spatial, temporal, and spectral) whose relationship between the array elements and the real world can be described by World Coordinate System (WCS) translations.

CosmoPower develops Bayesian inference pipelines that leverage machine learning to solve inverse problems in science. While the emphasis is on building algorithms to accelerate Bayesian inference in cosmology, the implemented methods allow for their application across a wide range of scientific fields. CosmoPower provides neural network emulators of matter and Cosmic Microwave Background power spectra, which can replace Boltzmann codes such as CAMB (ascl:1102.026) or CLASS (ascl:1106.020) in cosmological inference pipelines, to source the power spectra needed for two-point statistics analyses. This provides orders-of-magnitude acceleration to the inference pipeline and integrates naturally with efficient techniques for sampling very high-dimensional parameter spaces.

GAStimator implements a Python MCMC Gibbs-sampler with adaptive stepping. The code is simple, robust, and stable and well suited to high dimensional problems with many degrees of freedom and very sharp likelihood features. It has been used extensively for kinematic modeling of molecular gas in galaxies, but is fully general and may be used for any problem MCMC methods can tackle.

Scaling Relations Finder finds the scaling relations between magnetic field properties and observables for a model of galactic magnetic fields. It uses observable quantities as input: the galaxy rotation curve, the surface densities of the gas, stars and star formation rate, and the gas temperature to create galactic dynamo models. These models can be used to estimate parameters of the random and mean components of the magnetic field, as well as the gas scale height, root-mean-square velocity and the correlation length and time of the interstellar turbulence, in terms of the observables.

SMART (Spectral energy distributions Markov chain Analysis with Radiative Transfer models) implements a Bayesian Markov chain Monte Carlo (MCMC) method to fit the ultraviolet to millimeter spectral energy distributions (SEDs) of galaxies exclusively with radiative transfer models. The models constitute four types of pre-computed libraries, which describe the starburst, active galactic nucleus (AGN) torus, host galaxy and polar dust components.

candl (CMB Analysis With A Differentiable Likelihood) analyzes CMB power spectrum measurements using a differentiable likelihood framework. It is compatible with JAX (ascl:2111.002), though JAX is optional, allowing for fast and easy computation of gradients and Hessians of the likelihoods, and candl provides interface tools for working with other cosmology software packages, including Cobaya (ascl:1910.019) and MontePython (ascl:1805.027). The package also provides auxiliary tools for common analysis tasks, such as generating mock data, and supports the analysis of primary CMB and lensing power spectrum data.

Lenser estimates weak gravitational lensing signals, particularly flexion, from real survey data or realistically simulated images. Lenser employs a hybrid of image moment analysis and an Analytic Image Modeling (AIM) analysis. In addition to extracting flexion measurements by fitting a (modified Sérsic) model to a single image of a galaxy, Lenser can do multi-band, multi-epoch fitting. In multi-band mode, Lenser fits a single model to multiple postage stamps, each representing an exposure of a single galaxy in a particular band.

The anzu package offers two independent codes for hybrid Lagrangian bias models in large-scale structure. The first code measures the hybrid "basis functions"; the second takes measurements of these basis functions and constructs an emulator to obtain predictions from them at any cosmology (within the bounds of the training set). anzu is self-contained; given a set of N-body simulations used to build emulators, it measures the basis functions. Alternatively, given measurements of the basis functions, anzu should in principle be useful for constructing a custom emulator.

CARDiAC (Code for Anisotropic Redshift Distributions in Angular Clustering) computes the impact of anisotropic redshift distributions on a wide class of angular clustering observables. It supports auto- and cross-correlations of galaxy samples and cosmic shear maps, including galaxy-galaxy lensing. The anisotropy can be present in the mean redshift and/or width of Gaussian distributions, as well as in the fraction of galaxies in each component of multi-modal distributions. Templates of these variations can be provided by the user or simulated internally within the code.

sphereint calculates the numerical volume in a sphere. It provides a weight for each grid position based on whether or not it is in (weight = 1), out (weight = 0), or partially in (weight in between 0 and 1) a sphere of a given radius. A cubic cell is placed around each grid position and the volume of the cell in the sphere (assuming a flat surface in the cell) is calculated and normalized by the cell volume to obtain the weight.

CBiRd (Code for Bias tracers In Redshift space) provides correlators in the Effective Field Theory of Large-Scale Structure (EFTofLSS) in a ready-to-use pipeline for cosmological analysis of galaxy-redshift surveys data. It provides a core calculation package (C++BiRd), a Python implementation of a Taylor expansion of the power spectrum around a reference cosmology for efficient evaluation (TBiRd), and libraries to correct for observational systematics. CBiRd also provides MCMC samplers (MCBiRd) for a power spectrum and bispectrum analysis of galaxy-redshift surveys data based on emcee (ascl:1303.002), and can provide an earlybird pass to explore the cosmos with LSS surveys.

PRyMordial offers fast and precise evaluation of both the Big Bang Nucleosynthesis (BBN) light-element abundances and the effective number of relativistic degrees of freedom. It can be used within and beyond the Standard Model. The package calculates N_eff and helium-4, deuterium, helium-3 and lithium-7 abundances. PRyMordial corrects for QED plasma effects, neutron lifetime, and incomplete neutrino decoupling, and includes an optional module that re-elaborates all the ODE systems of the code in Julia.

Color transformations calculator determines the magnitude of a galaxy in a needed photometric band, given its color and magnitude in the original band. It supports various optical and near intrared surveys, including SDSS, DECaLS, DELVE, UKIDSS, VHS, and VIKING, and provides conversions for both total and aperture magnitudes with apertures of 1.5", 2" or 3" diameters. The source code, useful for performing bulk calculations, is available in Python and IDL; the calculator is also offered as a web service.

Quadratic Monte Carlo generates ensembles of models and confines fitness landscapes without relying on linear stretch moves; it works very efficiently for ring potential and Rosenbrock density. The method is general and can be implemented into any existing MC software, requiring only a few lines of code.

The ALeRCE anomaly detector cross-validates six anomaly detection algorithms for three classes (transient, periodic, and stochastic) of anomalous sources within the Zwicky Transient Facility (ZTF) data stream using the ALeRCE light curve features. A machine and deep learning-based framework is used for anomaly detection. For each class, a distinct anomaly detection model is constructed using only information about the known objects (i.e., inliers) for training. An anomaly score is computed using the probabilities to determine whether the light curve corresponds to a transient, stochastic, or periodic nature.

EVA (Excess Variability-based Age) computes the VarX values and VarX90 ages for a given list of stars. The package retrieves information from Gaia, performs basic var90 calculations, then calculates the age of the group in a given band or overall (by combining all three bands). EVA then analyzes and plots the results.

FLORAH generates the assembly history of halos using a recurrent neural network and normalizing flow model. The machine-learning framework can be used to combine multiple generated networks that are trained on a suite of simulations with different redshift ranges and mass resolutions. Depending on the training, the code recovers key properties, including the time evolution of mass and concentration, and galaxy stellar mass versus halo mass relation and its residuals. FLORAH also reproduces the dependence of clustering on properties other than mass, and is a step towards a machine learning-based framework for planting full merger trees.

BiaPy provides deep-learning workflows for a large variety of image analysis tasks, including 2D and 3D semantic segmentation, instance segmentation, object detection, image denoising, single image super-resolution, self-supervised learning and image classification. Though developed specifically for bioimages, it can be used for watershed-based instance segmentation for friends-of-friends proto-haloes.

ytree reads and works with merger tree data from multiple formats. An extension of yt (ascl:1011.022), which can analyze snapshots from cosmological simulations, ytree can be thought of as the yt of merger trees. ytree's online documentation lists supported formats; support for additional formats can be added, as in principle, any type of tree-like data where an object has one or more ancestors and a single descendant can be supported.

SuperLite produces synthetic spectra for astrophysical transient phenomena affected by circumstellar interaction. It uses Monte Carlo methods and multigroup structured opacity calculations for semi-implicit, semirelativistic radiation transport in high-velocity shocked outflows, and can reproduce spectra of typical Type Ia, Type IIP, and Type IIn supernovae. SuperLite also generates high-quality spectra that can be compared with observations of transient events, including superluminous supernovae, pulsational pair-instability supernovae, and other peculiar transients.

Magnification bias estimation estimates magnification bias for a galaxy sample with a complex photometric selection for the example of SDSS BOSS. The code works for CMASS and the LOWZ, z1 and z3 samples. A template for applying the approach to other surveys is included; requirements include a galaxy catalog that provides magnitudes (used for photometric selection) and the exact conditions used for the photometric selection.

LeHaMoC simulates high-energy astrophysical sources. It simulates the behavior of relativistic pairs, protons interacting with magnetic fields, and photons in a spherical region. The package contains numerous physical processes, including synchrotron emission and self-absorption, inverse Compton scattering, photon-photon pair production, and adiabatic losses. It also includes proton-photon pion production, proton-photon (Bethe-Heitler) pair production, and proton-proton collisions. LeHaMoC can model expanding spherical sources with a variable magnetic field strength. In addition, three types of external radiation fields can be defined: grey body or black body, power-law, and tabulated.

Photochem models the photochemical and climate composition of a planet's atmosphere. It takes inputs such as the stellar UV flux and atmospheric temperature structure to find the steady-state chemical composition of an atmosphere, or evolve atmospheres through time. Photochem also contains 1-D climate models and a chemical equilibrium solver.

Phazap post-processes gravitational-wave (GW) parameter estimation data to obtain the phases and polarization state of the signal at a given detector and frequency. It is used for low-latency identification of strongly lensed gravitational waves via their phase consistency by measuring their distance in the detector phase space. Phazap builds on top of the IGWN conda enviroment which includes the standard GW packages LALSuite (ascl:2012.021) and bilby (ascl:1901.011), and can be applied beyond lensing to test possible deviations in the phase evolution from modified theories of gravity and constrain GW birefringence.

This python code automatically detects solar active regions (AR). Based on morphological operation and region growing, it uses synoptic magnetograms from SOHO/MDI and SDO/HMI and calculates the parameters that characterize each AR, including the latitude and longitude of the flux-weighted centroid of two polarities and the whole AR, the area, and the flux of each polarity, and the initial and final dipole moments.

GRINN (Gravity Informed Neural Network) solves the coupled set of time-dependent partial differential equations describing the evolution of self-gravitating flows in one, two, and three spatial dimensions. It is based on physics informed neural networks (PINNs), which are mesh-free and offer a fundamentally different approach to solving such partial differential equations. GRINN has solved for the evolution of self-gravitating, small-amplitude perturbations and long-wavelength perturbations and, when modeling 3D astrophysical flows, provides accuracy on par with finite difference (FD) codes with an improvement in computational speed.

PowerSpecCovFFT computes the non-Gaussian (regular trispectrum and its shot noise) part of the analytic covariance matrix of the redshift-space galaxy power spectrum multipoles using an FFTLog-based method. The galaxy trispectrum is based on a tree-level standard perturbation theory but with a slightly different galaxy bias expansion. The code computes the non-Gaussian covariance of the power spectrum monopole, quadrupole, hexadecapole, and their cross-covariance up to kmax ~ 0.4 h/Mpc.

Faceted-HyperSARA images radio-interferometric wideband intensity data. Written in MATLAB, the library offers a collection of utility functions and scripts from data extraction from an RI measurement set MS Table to the reconstruction of a wideband intensity image over the field of view and frequency range of interest. The code achieves high precision imaging from large data volumes and supports data dimensionality reduction via visibility gridding and estimation of the effective noise level when reliable noise estimates are not available. Faceted-HyperSASA also corrects the w-term via w-projection and incorporates available compact Fourier models of the direction dependent effects (DDEs) in the measurement operator.

The phi-GPU (Parallel Hermite Integration on GPU) high-order N-body parallel dynamic code uses the fourth-order Hermite integration scheme with hierarchical individual block time-steps and incorporates external gravity. The software works directly with GPU, using only NVIDIA GPU and CUDA code. It creates numerical simulations and can be used to study galaxy and star cluster evolution.

Redback provides end-to-end interpretation and parameter estimation of electromagnetic transients. Using data downloaded by the code or provided by the user, the code processes the data into a homogeneous transient object. Redback implements several different types of electromagnetic transients models, ranging from simple analytical models to numerical surrogates, fits models implemented in the package or provided by the user, and plots lightcurves. The code can also be used as a tool to simulate realistic populations without having to fit anything, as models are implemented as functions and can be used to simulate populations. Redback uses Bilby (ascl:1901.011) for sampling and can easily switch samplers and likelihoods.

AutoPhOT (AUTOmated Photometry Of Transients) produces publication-quality photometry of transients quickly. Written in Python 3, this automated pipeline's capabilities include aperture and PSF-fitting photometry, template subtraction, and calculation of limiting magnitudes through artificial source injection. AutoPhOT is also capable of calibrating photometry against either survey catalogs (e.g., SDSS, PanSTARRS) or using a custom set of local photometric standards.

Exovetter is an open-source, pip-installable python package which calculates metrics on high cadence time series photometry to distinguish between exoplanet transit signals and false positives. The package standardizes the implementation of metrics developed for the TESS, Kepler, and K2 missions such as Odd-Even, Multiple Event Statistic, and Centroid Offset (see “Planetary Candidates Observed by Kepler. VIII.”, Thompson et al. 2018.). Metrics can be run individually or together as part of a pipeline. Exovetter also includes several visualizations to further evaluate the transits and metrics.

WinNet, a single zone nuclear reaction network, calculates many different nucleosynthesis processes, including r-process, nup-process, and explosive nucleosynthesis, and many more). It reads in a user-defined file with runtime parameters, then chooses the evolution mode, which is dependent on temperature. The temperature, density, and neutrino quantities are updated, after which the reaction network equations are solved numerically. If convergence is not achieved, the step size is halved and the iteration is repeated. Once convergence is reached, the output is generated and the time is evolved; the final output such as the final abundances and mass fractions are written.

MAKEE (MAuna Kea Echelle Extraction) reduces data from the HIRES and ESI instruments at Keck Observatory. It is optimized for the spectral extraction of single, unresolved point sources and is designed to run non-interactively using a set of default parameters. Taking the raw HIRES FITS files as input, the code determines the position (or trace) of each echelle order, defines the object and background extraction boundaries, optimally extracts a spectrum for each order, and computes wavelength calibrations. MAKEE produces FITS format "spectral images" (each row is a separate echelle order spectrum) and the data values are in arbitrary (relative) flux units. MAKEE will reduce data from all HIRES formats, including the single CCD format, the single CCD with Red and UV cross dispersers, and the current 3 CCD system. It can handle a variety of pixel binnings, including 1x1, 1x2, 1x4 (column x row).

Python Fully Automated TiRiFiC (pyFAT) wraps around the tilted ring fitting code (TiRiFiC, ascl:1208.008) to fully automate the process of fitting simple tilted ring models to line emission cubes. pyFAT is the successor to the IDL/GDL FAT (ascl:1507.011) code and offers improved handling and fitting as well as several new features. PyFAT fits simple rotationally symmetric discs with asymmetric warps and surface brightness distributions, providing a base model that can can be used in TiRiFiC to explore large scale motions. pyFAT delivers much more control over the fitting procedure, which is made possible by the new modular setup and the use of omegaconf for the input and default settings.

pycosie is analysis code used for Technicolor Dawn (TD), a Gadget-3 derived cosmological radiative SPH simulation suite. The target analyses are to complement what is done with TD and other analysis software in its suite. pycosie creates power spectrum from generated Lyman-alpha forests spectra, links absorbers to potential host galaxies, grids gas information for each galaxy, and reads specific output files from software such as Rockstar (ascl:1210.008) and SKID (ascl:1102.020).

Forklens measures weak gravitational lensing signal using a deep-learning methoe. It measures galaxy shapes (shear) and corrects the smearing of the point spread function (PSF, an effect from either/both the atmosphere and optical instrument). It contains a custom CNN architecture with two input branches, fed with the observed galaxy image and PSF image, and predicts several features of the galaxy, including shape, magnitude, and size. Simulation in the code is built directly upon GalSim (ascl:1402.009).

BaCoN (BAyesian COsmological Network) trains and tests Bayesian Convolutional Neural Networks in order to classify dark matter power spectra as being representative of different cosmologies, as well as to compute the classification confidence. It supports the following theories: LCDM, wCDM, f(R), DGP, and a randomly generated class. Additional cosmologies can be easily added.

provabgs infers full posterior distributions of galaxy properties for galaxies in the DESI Bright Galaxy Survey using state-of-the-art Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and photometry. provabgs includes a state-of-the-art stellar population synthesis (SPS) model based on non-parametric prescription for star formation history, a metallicity history that varies over the age of the galaxy, and a flexible dust prescription. It has a neural network emulator for the SPS model that enables accelerated inference. Full posteriors of the 12 SPS parameters can be derived in ~10 minutes. The emulator is currently designed for galaxies from 0 < z < 0.6. provabgs also includes a Bayesian inference pipeline that is based on zeus (ascl:2008.010).

GRDzhadzha evolves matter on curved spacetimes with an analytic time and space dependence. Written in C++14, it uses hybrid MPI/OpenMP parallelism to achieve good performance. The code is based on publicly available 3+1D numerical relativity code GRChombo (ascl:2306.039) and inherits all of the capabilities of the main GRChombo code, which uses the Chombo library for adaptive mesh refinement.

RealSim generates survey-realistic synthetic images of galaxies from hydrodynamical simulations of galaxy formation and evolution. The main functionality of this code inserts "idealized" simulated galaxies into Sloan Digital Sky Survey (SDSS) images in such a way that the statistics of sky brightness, resolution, and crowding are matched between simulated galaxies and observed galaxies in the SDSS. The suite accepts idealized synthetic images in calibrated AB surface brightnesses and rebins them to the desired redshift and CCD angular scale; RealSim can add Poisson noise, if desired, by adopting generic values of photometric calibrations in survey fields. Images produced by the suite can be inserted into real image fields to incorporate real skies, PSF degradation, and contamination by neighboring sources in the field of view. The RealSim methodology can be applied to any existing galaxy imaging survey.

ATM (Asteroid Thermal Modeling) models asteroid flux measurements to estimate an asteroid's size, surface temperature distribution, and emissivity, and creates model spectral energy distributions for the different thermal models. After downloading lookup tables for relevant models, it can also fit observations of asteroids.

UFalcon rapidly post-processes N-body code output into signal maps for many different cosmological probes. The package is able to produce maps of weak-lensing convergence, linear-bias galaxy over-density, cosmic microwave background (CMB) lensing convergence and the integrated Sachs-Wolfe temperature perturbation given a set of N-body lightcones. It offers high flexibility for lightcone construction, such as user-specific survey-redshift ranges, redshift distributions and single-source redshifts. UFalcon also computes the galaxy intrinsic alignment signal, which can be treated as an additive component to the cosmological signal.

bigfile stores data from cosmology simulations from HPC systems and beyond. It provides a hierarchical structure of data columns via File, Dataset and Column. A Column stores a two dimensional table. Numerical typed columns are supported; attributes can be attached to a Column and both numerical attributes and string attributes are supported. Type casting is performed on-the-fly if read/write operations request a different data type than the file has stored.

Fof uses the friends-of-friends method to find groups. A particle belongs to a friends-of-friends group if it is within some linking length of any other particle in the group. After all such groups are found, those with less than a specified minimum number of group members are rejected. The program takes input files in the TIPSY (ascl:1111.015) binary format and produces a single ASCII output file called fof.grp. This output file is in the TIPSY array format and contains the group number to which each particle belongs. A group number of zero means that the particle does not belong to a group. The fof.grp file can be read in by TIPSY and used to color each particle by group number to visualize the groups. Simulations with periodic boundary conditions can also be handled by fof by specifying the period in each dimension on the command line.

cola_halo generates hundreds of realizations on the fly. This parallel cosmological N-body simulation code generates random Gaussian initial condition using 2LPTIC (ascl:1201.005), time evolves N-body particles with colacode (ascl:1602.021), and finds dark-matter halos with the Friends-of-Friends code (ascl:2407.012).

PFFT computes massively parallel, fast Fourier transformations on distributed memory architectures. PFFT can be understood as a generalization of FFTW-MPI (ascl:1201.015) to multidimensional data decomposition; in fact, using PFFT is very similar to FFTW. The library is written in C and MPI; a Fortran interface is also available.

AstroCLIP performs contrastive pre-training between two different kinds of astronomical data modalities (multi-band imaging and optical spectra) to yield a meaningful embedding space which captures physical information about galaxies and is shared between both modalities. The embeddings can be used as the basis for competitive zero- and few-shot learning on a variety of downstream tasks, including similarity search, redshift estimation, galaxy property prediction, and morphology classification.

Flash-X simulates physical phenomena in several scientific domains, primarily those involving compressible or incompressible reactive flows, using Eulerian adaptive mesh and particle techniques. It derives some of its solvers from and is a descendant of FLASH (ascl:1010.082). Flash-X has a new framework that relies on abstractions and asynchronous communications for performance portability across a range of heterogeneous hardware platforms, including exascale machines. It also includes new physics capabilities, such as the Spark general relativistic magnetohydrodynamics (GRMHD) solver, and supports interoperation with the AMReX mesh framework, the HYPRE linear solver package, and the Thornado neutrino radiation hydrodynamics package, among others.

Heimdall uses direct, tree, and sub-band dedispersion algorithms on massively parallel computing architectures (GPUs) to speed up real-time detection of radio pulsar and other transient events.

ELISA is a Python library designed for efficient spectral modeling and robust statistical inference. With user-friendly interface, ELISA streamlines the spectral analysis workflow.

The modeling framework of ELISA is flexible, allowing users to construct complex models by combining models of ELISA and XSPEC, as well as custom models. Parameters across different model components can also be linked. The models can be fitted to the spectral datasets using either Bayesian or maximum likelihood approaches. For Bayesian fitting, ELISA incorporates advanced Markov Chain Monte Carlo (MCMC) algorithms, including the No-U-Turn Sampler (NUTS), nested sampling, and affine-invariant ensemble sampling, to tackle the posterior sampling problem. For maximum likelihood estimation (MLE), ELISA includes two robust algorithms: the Levenberg-Marquardt algorithm and the Migrad algorithm from Minuit. The computation backend is based on Google's JAX, a high-performance numerical computing library, which can reduce the runtime for fitting procedures like MCMC, thereby enhancing the efficiency of analysis.

After fitting, goodness-of-fit assessment can be done with a single function call, which automatically conducts posterior predictive checks and leave-one-out cross-validation for Bayesian models, or parametric bootstrap for MLE. These methods offer greater accuracy and reliability than traditional fit-statistic/dof measures, and thus better model discovery capability. For comparing multiple candidate models, ELISA provides robust Bayesian tools such as the Widely Applicable Information Criterion (WAIC) and the Leave-One-Out Information Criterion (LOOIC), which are more reliable than AIC or BIC. Thanks to the object-oriented design, collecting the analysis results should be simple. ELISA also provide visualization tools to generate ready-for-publication figures.

ELISA is an open-source project and community contributions are welcome and greatly appreciated.

The photGalIMF code calculates the evolution of stellar mass and luminosity for a galaxy model, based on the PARSEC stellar evolution model (ascl:1502.005). It requires input lists specifying the age, mass, metallicity, and initial mass function (IMF) of single stellar populations. These input parameters can be provided by the companion galaxy chemical simulation code GalIMF (ascl:1903.010), which generates realistic sets of inputs.

pony3d statistically identifies islands of contiguous emission inside a three-dimensional volume. The primary functionality is the rapid and reliable creation of masks for the deconvolution of radio interferometric radio spectral line emission. It has been designed to run on the output of the wsclean imager (ascl:1408.023) whereby the individual FITS image per frequency plane enables a high degree of parallelism, but can work on any image set providing this criterion is met. Single channel island rejection is offered, along with 3D mask dilation and boxcar averaging. pony3d is also a prototype source-finding and extraction tool.

The High-Resolution Imaging and Spectroscopy of Exoplanets (HiRISE) instrument at the Very Large Telescope (VLT) combines the exoplanet imager SPHERE with the high-resolution spectrograph CRIRES using single-mode fibers. HiRISE has been designed to enable the characterization of known, directly-imaged planetary companions in the H band at a spectral resolution on the order of R = λ/∆λ = 140 000. The hipipe package is a custom python pipeline used to reduce the HiRISE data and produce high-level science products that can be used for astrophysical interpretation.

Package‑X instantly solves one loop Feynman integrals in full generality. Written in Mathematica and extensively tested and adopted, the package computes dimensionally regulated one-loop integrals with up to four distinct propagators of arbitrarily high rank, calculates traces of Dirac matrices in d dimensions for closed fermion loops, or carries out Dirac algebra for open fermion lines. Package‑X also generates analytic results for any kinematic configuration (e.g., at zero external momentum or physical threshold) for real masses and external invariants, provides analytic expressions for UV-divergent, IR-divergent and finite parts either separately or all together, and computes discontinuities across cuts of one-loop integrals, among other tasks.

Sharpened dimensionality reduction (SDR) sharpens original data before dimensionality reduction to create visually segregrated sample clusters. user-guided labeling. Each distinct cluster can then be labeled and used to further analyze an otherwise unlabeled data set. Written in C++, SDR scales well with large high-dimensional data.

pySDR performs local gradient clustering-based sharpened dimensionality reduction (SDR). The library uses the C++ LGCDR_v1 code as its backend.

SHARC (SHArpened Dimensionality Reduction and Classification) performs local gradient clustering-based sharpened dimensionality reduction (SDR) using neural network projections and uses these projections to make classifications. The library also contains functions for finding the optimal SDR parameters and for consolidating classification results obtained through multiple classifiers. It requires pySDR (ascl:2408.002). SHARC provides accurate and physically insightful classification of astronomical objects based on their broadband colors.

Global mm-VLBI Array (GMVA) observations are accompanied by a lot of metadata (i.e., the so-called 'ANTAB' files) that contain the system temperature (Tsys) and the gain values of the individual GMVA antennas. These data are required for the amplitude calibration of GMVA data which is an essential part in the data reduction. Unfortunately, Tsys measurements in the ANTAB files are not perfect and there are almost always erroneous values in some of the ANTAB files (particularly in the VLBA data). This could lead to incorrect results in the amplitude calibration and thus need to be corrected with proper data inspection/treatment. However, every GMVA station provides the ANTAB file in their own data format which makes the examination tricky. AntabGMVA was designed to resolve these issues and allows GMVA users to manage the GMVA ANTAB files easily and efficiently. Using AntabGMVA, one can perform extraction/inspection/visualization/correction of the Tsys data from the ANTAB files and finally generate one single ANTAB file which includes all the final products.

Sailfish simulates accreting binary systems, including binary protostars, post-AGN stellar binaries, mass-transferring X-ray binaries, and double black hole systems. The binary components are "on the grid" rather than excised, and are evolved according to the Kepler two-body problem, modified to account for gravitational wave losses or self-consistent forcing from the orbiting gas. The solvers are shock-capturing and are second order accurate in space and time. Gravity is fully Newtonian. Thermodynamics can be treated using a gamma-law equation of state with a blackbody cooling term, or in the locally isothermal approximation, in which the gas temperature is set to a constant times the local free-fall speed. Sailfish is fully Cartesian and has extensive diagnostic capabilities to facilitate accurate calculations of gas-driven orbital evolution or the extraction of electromagnetic disk signatures. The code is extremely efficient, reaching more than one billion zone updates per second on an NVIDIA A100 GPU, enabling extremely high resolution of complex flows around the binary components.

Astronify contains tools for sonifying astronomical data, specifically data series. Data series sonification takes a data table and maps one column to time, and one column to pitch. This technique is commonly used to sonify light curves, where observation time is scaled to listening time and flux is mapped to pitch. While Astronify’s sonification uses the columns “time” and “flux” by default, any two columns can be supplied and a sonification created.

Sonification extends the Astronify software (ascl:2408.005) to sonify a spatially distributed dataset. The package contains scripts to convert images into scatterplots and sonifications. The reproduce_image.py script takes an image file and reproduces it as a scatterplot by converting the input image to grayscale, extracting pixel values and generating scatter data based on these values, and then plotting the scatter data to create a visual representation of the image. The sonifications script converts the scatterplot data into an audio series and adjusts the note spacing and sonification range to customize an auditory representation. Sonification accepts images in PNG and JPG formats.

LADDER (Learning Algorithm for Deep Distance Estimation and Reconstruction) reconstructs the “cosmic distance ladder” by analyzing sequential cosmological data; it can also be applied to other sequential datasets with associated covariance information. It uses the apparent magnitude data from the Pantheon Type Ia supernovae compilation, fully incorporating covariance information to accurately predict mean values and uncertainties. It offers model-independent consistency checks for datasets such as Baryon Acoustic Oscillations (BAO) and can calibrate high-redshift datasets such as Gamma Ray Bursts (GRBs) without assuming any underlying cosmological model. Additionally, LADDER serves as a model-independent mock catalog generator for forecast-based cosmological studies.

HaloFlow uses a machine learning approach to infer Mh and stellar mass, M∗, using grizy band magnitudes, morphological properties quantifying characteristic size, concentration, and asymmetry, total measured satellite luminosity, and number of satellites.

Cue interprets nebular emission across a wide range of ionizing conditions of galaxies. The software, based on Cloudy (ascl:9910.001), emulates a neural net. It does not require a specific ionizing spectrum as a source, instead approximating the ionizing spectrum with a 4-part piece-wise power-law. Along with the flexible ionizing spectra, Cue allows freedom in [O/H], [N/O], [C/O], gas density, and total ionizing photon budget.

BELTCROSS2 calculates the closest approaches of asteroid to the mean orbits of meteoroid streams. It is especially useful to check if an asteroid, which was observed to become active, passed through a meteoroid stream, and through which stream, a short time before the beginning of the activity. The basic characteristics of the closest encounter of the asteroid with the stream are provided by BELTCROSS2.

M_SMiLe computes an approximation of the probability of magnification for a lens system consisting of microlensing by compact objects within a galaxy cluster. It specifically focuses on the scenario where the galaxy cluster is strongly lensing a background galaxy and the compact objects, such as stars, are sensitive to this microlensing effect. The microlenses responsible for this effect are stars and stellar remnants, though exotic objects such as compact dark matter candidates (including PBHs and axion mini-halos) can contribute to this effect.

RadioSED uses nested sampling to perform a Bayesian analysis of radio SEDs constructed from radio flux density measurements obtained as part of large area surveys (or in some limited cases, as part of targeted followup campaigns). It is a pure Python implementation, and is essentially a wrapper around Bilby (ascl:1901.011), the Bayesian inference library. RadioSED uses dynesty (ascl:1809.013) to perform the sampling steps, though other samplers could also be used. Users can make use of a pre-defined set of models and surveys from which to draw flux density measurements, or they can define their own models and provide their own input flux density measurements. All flux density measurements are referenced against the RACS-LOW survey, and source names and IDs from the survey catalogue are used as identifiers.

GRBoondi simulates generalized Proca fields on arbitrary analytic fixed backgrounds; it is based on the publicly available 3+1D numerical relativity code GRChombo (ascl:2306.039). GRBoondi reduces the prerequisite knowledge of numerical relativity and GRChombo in the numerical studies of generalized Proca theories. The main steps to perform a study are inputting the additions to the equations of motion beyond the base Proca theory; GRBoondi can then automatically incorporate the higher-order terms in the simulation. The code is written entirely in C++14 and uses hybrid MPI/OpenMP parallelism. GRBoondi inherits all of the capabilities of the main GRChombo code, which makes use of the Chombo library (ascl:1202.008) for adaptive mesh refinement.

21cmFirstCLASS extends 21cmFAST (ascl:1102.023) and interfaces with CLASS (ascl:1106.020) to generate initial conditions at recombination that are consistent with the input cosmological model. These initial conditions can be set during the time of recombination, allowing one to compute the 21cm signal (and its spatial fluctuations) throughout the dark ages, as well as in the proceeding cosmic dawn and reionization epochs, just as in the standard 21cmFAST. 21cmFirstCLASS tracks both the CDM density field δ_c as well as the baryons density field δ_b. In addition, the user interface in 21cmFirstCLASS has been improved and allows one to easily plot the 21cm power spectrum while including noise from the output of 21cmSense (ascl:1609.013).

SAQQARA analyzes stochastic gravitational wave background signals. This Simulation-based Inference (SBI) library is built on top of the swyft code (ascl:2302.016), which implements neural ratio estimation to efficiently access marginal posteriors for all parameters of interest. Simulation-based inference combined with implicit marginalization (over nuisance parameters) has been shown to be well suited for SGWB data analysis.