Mosaic is a software package that consists of an interferometric pattern simulator and characterizer, an optimized tiling generator and a beamforming weights calculator. It is being used in the filter-banking beamformer in MeerKAT telescope and more than 200 pulsars have been discovered from the multiple beam observations supported by Mosaic.

Falcon-DM simulates intermediate mass ratio inspirals in DM spikes. This lightweight N-body code is written in C++ and is specifically tuned for simulating IMRIs embedded in dark matter (DM) spikes. It features a 2nd order Drift-Kick-Drift integrator using the symplectic HOLD scheme and symmetrized, individual, time-steps for accurate time-integration. Falcon-DM also offers post-Newtonian (PN) effects up to PN2.5 using the auxiliary velocity algorithm.

Heracles manages harmonic-space statistics on the sphere. It takes catalogs of positions and function values on the sphere and turns them into angular power spectra and mixing matrices. Heracles is both a Python library, to be used in notebooks or data processing pipelines, and a tool for running measurements from the command line using a configuration file.

fastPTA forecasts the sensitivity of future Pulsar Timing Array (PTA) configurations and assesses constraints on Stochastic Gravitational Wave Background (SGWB) parameters. The code can generate mock PTA catalogs with noise levels compatible with current and future PTA experiments. These catalogs can then be used to perform Fisher forecasts of MCMC simulations.

StellarSpectraObservationFitting (SSOF) measures radial velocities and creates data-driven models (with fast, physically-motivated Gaussian Process regularization) for the time-variable spectral features for both the telluric transmission and stellar spectrum measured by Extremely Precise Radial Velocity (EPRV) spectrographs (while accounting for the wavelength-dependent instrumental line-spread function). Written in Julia, SSOF provides two methods for estimating the uncertainties on the RVs and model scores based on the photon uncertainties in the original data. For quick estimates of the uncertainties, the code looks at the local curvature of the likelihood space; the second method for estimating errors is via bootstrap resampling.

Gaspery uses the Fisher Information Matrix (FIM) to evaluate different radial velocity (RV) observing strategies; this assists observational exoplanet astronomers in constructing the observing strategy that maximizes information (or minimizes uncertainty) on the RV semi-amplitude K. The code is flexible and generalizable, however, and can maximize information on any free parameter from any model, given a time series support (x-axis).

Kamodo provides access to, interpolation of, and visualization of space weather models and data. The code allows model developers to represent simulation results as mathematical functions which may be manipulated directly. As the software does not generate model outputs, users must acquire the desired model outputs before these outputs can be functionalized by the software. Kamodo handles unit conversion transparently and supports interactive science discovery through Jupyter notebooks with minimal coding.

CloudCovErr.jl debiases and improves error bar estimates for photometry on top of structured filamentary backgrounds. It first estimates the covariance matrix of the residuals from a previous photometric model and then computes corrections to the estimated flux and flux uncertainties. Using an infilling technique to estimate the background and its uncertainty dramatically improves flux and flux uncertainty estimates for stars in images of fields with significant nebulosity.

ARK implements Computational Fluid Dynamics applications, such as Euler and all-Mach regime, on a Cartesian grid with MPI+Kokkos. It provides a performance-portable Kokkos implementation for compressible hydrodynamics and performs simulations of convection without any approximation of Boussinesq nor anelastic type. It adapts an all-Mach number scheme into a well-balanced scheme for gravity, which preserves arbitrary discrete equilibrium states up to the machine precision. The low-Mach correction in the numerical flux allows ARK to be more precise in the low-Mach regime; the code is well suited for studying highly stratified and high-Mach convective flows.

The 1D radiative-equilibrium model Exo-REM simulates young gas giants far from their star and brown dwarfs. Fluxes are calculated using the two-stream approximation assuming hemispheric closure. The radiative-convective equilibrium is solved assuming that the net flux (radiative + convective) is conservative. The conservation of flux over the pressure grid is solved iteratively using a constrained linear inversion method. Rayleigh scattering from H2, He, and H2O, as well as absorption and scattering by clouds (calculated from extinction coefficient, single scattering albedo, and asymmetry factor interpolated from precomputed tables for a set of wavelengths and particle radii), are also taken into account.

DGEM compares different computation methods for three-dimensional dust continuum radiative transfer. This simple code is based on mcpolar, translated to C++, and refactored to realize and compare radiative transfer techniques, namely Monte Carlo, Quasi-Monte-Carlo, and the Directions Grid Enumeration Method (DGEM). DGEM uses precalculated directions of the photons propagation instead of the random ones to speed up the calculations process. The code also offers a gnuplot script for plotting the resulting images.

1. Configure the basic environment
2. Modify the config file
3. Run auto_task_scheduler.py

lensitbiases is an rFFT-based N1 lensing bias calculation and tests. It is tuned for TT, P-only or MV (GMV) like quadratic estimators. It performs rFFT-based N1 and N1 matrix calculations in ~ O(ms) time per lensing multipole for Planck-like config, which allows on-the-fly evaluation of the bias. It also calculates 5 rFFT's of moderate size per L for N1 TT, 20 for PP, and 45 for MV or GMV. lensitbiases is not particularly efficient for low lensing L's, since in this case one must use large boxes.

DIRTY (DustI Radiative Transfer, Yeah!) computes the radiative transfer and dust emission from arbitrary distributions of dust illuminated by arbitrary distributions of sources (usually stars). It uses Monte Carlo methods to solve the radiative transfer problem in full 3D including non-equilibrium and equilibrium thermal dust emission. As are other similar models, DUSTY is computationally intensive; as a result, it is written in C++.

solar-vSI performs Monte Carlo integration of multi-body phase space efficiently. The calculation of solar antineutrino spectra from 8B decay requires the integration of five-body phase space. Though there is no simple analytical approach to this problem, recursive relations can be used to facilitate numerical evaluations.

measure_extinction measures extinction due to dust absorbing photons or scattering photons out of the line-of-sight. Extinction applies to the case for a star seen behind a foreground screen of dust. This package provides the tools to measure dust extinction curves using observations of two effectively identical stars, differing only in that one is seen through more dust than the other.

Forcepho infers the fluxes and shapes of galaxies from astronomical images. It models the appearance of multiple sources in multiple bands simultaneously and compares to observed data via a likelihood function. Gradients of this likelihood allow for efficient maximization of the posterior probability or sampling of the posterior probability distribution via Hamiltonian Monte Carlo. The model intrinsic galaxy shapes and positions are shared across the different bands, but the fluxes are fit separately for each band. Forcepho does not perform detection; initial locations and (very rough) parameter estimates must be supplied by the user.

BayeSED implements full Bayesian interpretation of spectral energy distributions (SEDs) of galaxies and AGNs. It performs Bayesian parameter estimation using posteriori probability distributions (PDFs) and Bayesian SED model comparison using Bayesian evidence. Its latest version BayeSED3 supports various built-in SED models and can emulate other SED models using machine learning techniques.

iPIC3D performs kinetic plasma simulations at magnetohydrodynamics time scales. This three-dimensional parallel code uses the implicit Particle-in-Cell method; implicit integration in time of the Vlasov–Maxwell system removes the numerical stability constraints. Written in C++, iPIC3D can be run with CUDA acceleration and supports MPI, OpenMP, and multi-node multi-GPU simulations.

vortex-p analyzes the velocity fields of astrophysical simulations of different natures (for example, SPH, moving-mesh, and meshless) usually spanning many orders of magnitude in scales involved. The code performs Helmholtz-Hodge decomposition (HHD); that is, it can decompose the velocity field into a solenoidal and an irrotational/compressive part Helmholtz-Hodge decomposition. vortex-p internally uses an AMR representation of the velocity field and can, in principle, capture the full dynamical range of the simulation. The package can also perform Reynolds decomposition (i.e., the decomposition of the velocity field into a bulk and a turbulent part). This is achieved by means of a multi-scale filtering of the velocity field, where the filtering scale around each point is determined by the local flow properties. vortex-p expands the vortex (ascl:2206.001) code, which had been coupled to the outputs of the MASCLET code, to a fully stand-alone tool capable of working with the outcomes of a broad range of simulation methods.

pysymlog provides utilities for binning, normalizing colors, wrangling tick marks, and other tasks, in symmetric logarithm space. For numbers spanning positive and negative values, the code works in log scale with a transition through zero, down to some threshold. This is useful for representing data that span many scales such as standard log-space that include values of zero or even negative values. pysymlog provides convenient functions for creating 1D and 2D histograms and symmetric log bins, generating logspace-like arrays through zero and managing matplotlib major and minor ticks in symlog space, as well as bringing symmetric log scaling functionality to plotly.

This paper introduces RadioSunPy, an open-source Python package developed for accessing, visualizing, and analyzing multi-band radio observations of the Sun from the RATAN-600 solar complex. The advancement of observational technologies and software for processing and visualizing spectro-polarimetric microwave data obtained with the RATAN-600 radio telescope opens new opportunities for studying the physical characteristics of solar plasma at the levels of the chromosphere and corona. These levels remain some difficult to detect in the ultraviolet and X-ray ranges. The development of these methods allows for more precise investigation of the fine structure and dynamics of the solar atmosphere, thereby deepening our understanding of the processes occurring in these layers. The obtained data also can be utilized for diagnosing solar plasma and forecasting solar activity. However, using RATAN-600 data requires extensive data processing and familiarity with the RATAN-600. The package offers comprehensive data processing functionalities, including direct access to raw data, essential processing steps such as calibration and quiet Sun normalization, and tools for analyzing solar activity. This includes automatic detection of local sources, identifying them with NOAA (National Oceanic and Atmospheric Administration) active regions, and further determining parameters for local sources and active regions. By streamlining data processing workflows, RadioSunPy enables researchers to investigate the fine structure and dynamics of the solar atmosphere more efficiently, contributing to advancements in solar physics and space weather forecasting.

ysoisochrone is a Python3 package that handles the isochrones for young stellar objects (YSOs), and utilize isochrones to derive the stellar mass and ages. Our primary method is a Bayesian inference approach, and the Python code builds on the IDL version developed in Pascucci et al. (2016). The code estimates the stellar masses, ages, and associated uncertainties by comparing their stellar effective temperature, bolometric luminosity, and their uncertainties with different stellar evolutionary models, including those specifically developed for YSOs. User-developed evolutionary tracks can also be utilized when provided in the specific format described in the code documentation.

The kete tools are intended to enable the simulation of all-sky surveys of solar system objects. This includes multi-body physics orbital dynamics, thermal and optical modeling of the objects, as well as field of view and light delay corrections. These tools in conjunction with the Minor Planet Centers (MPC) database of known asteroids can be used to not only plan surveys but can also be used to predict what objects are visible for existing or past surveys.

The primary goal for kete is to enable a set of tools that can operate on the entire MPC catalog at once, without having to do queries on specific objects. It has been used to simulate over 10 years of survey time for the NEO Surveyor mission using 10 million main-belt and near-Earth asteroids.

GalCraft creates mock integral-field spectroscopic (IFS) observations of the Milky Way and other hydrodynamical/N-body simulations. It conducts all the procedures from inputting data and spectral templates to the output of IFS data cubes in FITS format. The produced mock data cubes can be analyzed in the same way as real IFS observations by many methods, particularly codes like Voronoi binning (ascl:1211.006), pPXF (ascl:1210.002), line-strength indices, or a combination of them (e.g., the GIST pipeline, ascl:1907.025). The code is implemented using Python-native parallelization. GalCraft will be particularly useful for directly comparing the Milky Way with other MW-like galaxies in terms of kinematics and stellar population parameters and ultimately linking the Galactic and extragalactic to study galaxy evolution.

pyRRG measures the 2nd and 4th order moments using a TinyTim model to correct for PSF distortions. The code is invariant to the number exposures and orientation of the drizzle images. pyRRG uses a machine learning algorithm to automatically classify stars and galaxies; this can also be done manually if greater accuracy is needed.

Padé simulates protoplanetary disk hydrodynamics in cylindrical coordinates. Written in Fortran90, it is a finite-difference code and the compact 4th-order standard Padé scheme is used for spatial differencing. Padé differentiation is known to have spectral-like resolving power. The z direction can be periodic or non-periodic. The 4th order Runge-Kutta is used for time advancement. Padé implements a version of the FARGO technique to eliminate the time-step restriction imposed by Keplerian advection, and capturing of shocks that are not too strong can be done by using artificial bulk viscosity.

PySR performs Symbolic Regression; it uses machine learning to find an interpretable symbolic expression that optimizes some objective. Over a period of several years, PySR has been engineered from the ground up to be (1) as high-performance as possible, (2) as configurable as possible, and (3) easy to use. PySR is developed alongside the Julia library SymbolicRegression.jl, which forms the powerful search engine of PySR. Symbolic regression works best on low-dimensional datasets, but one can also extend these approaches to higher-dimensional spaces by using "Symbolic Distillation" of Neural Networks. Here, one essentially uses symbolic regression to convert a neural net to an analytic equation. Thus, these tools simultaneously present an explicit and powerful way to interpret deep neural networks.

WISE2MBH uses infrared cataloged data from the Wide-field Infrared Survey Explorer (WISE) to estimate the mass of supermassive black holes (SMBH). It implements a Monte Carlo approach for error propagation, considering mean photometric errors from WISE magnitudes, errors in fits of scaling relations used and scatter of those relations, if available.

PyExoCross, a Python adaptation of ExoCross (ascl:1803.014), post-processes molecular line lists generated by ExoMol, HITRAN, and HITEMP and other similar initiatives. It generates absorption and emission spectra and other properties, including partition functions, specific heats, and cooling functions, based on molecular line lists. The code also calculates cross sections with four line profiles: Doppler, Gaussian, Lorentzian, and Voigt. PyExoCross can convert data format between ExoMol and HITRAN, and supports importing and exporting line lists in the ExoMol and HITRAN/HITEMP formats.

GASTLI (GAS gianT modeL for Interiors) calculates the interior structure models for gas giants exoplanets. The code computes mass-radius curves, thermal evolution curves, and interior composition retrievals to fit a interior structure model to your mass, radius, age, and if available, atmospheric metallicity data. GASTLI can also plot the results, including internal and atmospheric profiles, a pressure-temperature diagram, mass-radius relations, and thermal evolution curves.

symbolic_pofk provides simple Python functions and a Fortran90 routine for precise symbolic emulations of the linear and non-linear matter power spectra and for the conversion σ 8 ↔ A s as a function of cosmology. These can be easily copied, pasted, and modified to other languages. Outside of a tested k range, the fit includes baryons by default; however, this can be switched off.

planetMagFields accesses and analyzes information about magnetic fields of planets in our solar system and visualizes them in both 2D and 3D. The code provides access to properties of a planet, such as dipole tilt, Gauss coefficients, and computed radial magnetic field at surface, and has methods to plot the field and write a vts file for 3D visualization. planetMagFields can be used to produce both 2D and 3D visualizations of a planetary field; it also provides the option of potential extrapolation.

The software framework AMReX is designed for building massively parallel block-structured adaptive mesh refinement (AMR) applications. Key features of AMReX include C++ and Fortran interfaces; 1-, 2- and 3-D support; and support for cell-centered, face-centered, edge-centered, and nodal data. The framework also supports hyperbolic, parabolic, and elliptic solves on hierarchical adaptive grid structure, optional subcycling in time for time-dependent PDEs, and parallelization via flat MPI, OpenMP, hybrid MPI/OpenMP, or MPI/MPI, and parallel I/O. AMReX supports the plotfile format with AmrVis, VisIt (ascl:1103.007), ParaView (ascl:1103.014), and yt (ascl:1011.022).

ClassiPyGRB downloads, processes, visualizes, and classifies GRBs in the Swift/BAT database. Users can query light curves for any GRB and use tools to preprocess the data, including noise/duration reduction and interpolation. The package provides a set of facilities and tutorials for classifying GRBs based on their light curves using a method based on a dimensionality reduction of the data using t-Distributed Stochastic Neighbour Embedding (TSNE); results are visualized using a Graphical User Interface (GUI). ClassiPyGRB also plots and animates the results of the TSNE analysis for a deeper hyperparameter grid search.

BeyonCE (Beyond Common Eclipsers) explores the large parameter space of eclipsing disc systems. The fitting code reduces the parameter space encompassed by the transit of circumsecondary disc (CSD) systems with azimuthally symmetric, non-uniform optical-depth profiles to constrain the size and orientation of discs with a complex sub-structure. BeyonCE does this by rejecting disc geometries that do not reproduce the measured gradients within their light curves.

resonances identifies mean-motion resonances of small bodies. It uses the REBOUND integrator (ascl:1110.016) and automatically identifies two-body and three-body mean-motion resonance in the Solar system. The package can be used for other possible planetary systems, including exoplanets. resonances accurately differentiates different types of resonances (pure, transient, uncertain) and provides an interface for mass tasks, such as finding resonant areas in a planetary system. The software can also plot time series and periodograms.

cloudyfsps is a Python interface between FSPS (ascl:1010.043) and Cloudy (ascl:9910.001). It compiles FSPS models for use as ionizing sources (Stellar SED grids) within Cloudy and generates Cloudy input files, single-parameter or grids of parameters. It runs Cloudy models in parallel and formats the output, which is nebular continuum and nebular line emission, for FSPS input and for explorative manipulation and plotting within Python. cloudyfsps includes pre-packaged plots for BPT diagrams (NII, SII, OI, OII) with observed data from HII regions and SDSS galaxies, and also provides comparisons with MAPPINGS III (ascl:1306.008) models.

Stardust extracts galaxy properties by fitting their multiwavelength data to a set of linearly combined templates. This Python package brings three different families of templates together: 1.) UV+Optical emission from dust unobscured stellar light; 2.) AGN heated dust in the MIR; and 3.) IR dust reprocessed stellar light in the NIR-FIR. Stardust's template fitting does not rely on energy balance. As a result, the total luminosity of dust obscured and dust unobscured stellar light do not rely on each other, and it is possible to fit objects such as SMGs where the energy balance approach might not be applicable.

PICASSO (Python Inpainter for Cosmological and AStrophysical SOurces) provides a suite of inpainting methodologies to reconstruct holes on images (128x128 pixels) extracted from a HEALPIX map. Three inpainting techniques are included; these are divided into two main groups: diffusive-based methods (Nearest-Neighbors), and learning-based methods that rely on training DCNNs to fill the missing pixels with the predictions learned from a training data-set (Deep-Prior and Generative Adversarial Networks). PICASSO also provides scripts for projecting from full sky HEALPIX maps to flat thumbnails images, performing inpainting on GPUs and parallel inpainting on multiple processes, and for projecting from flat images to HEALPIX. Pretrained models are also included.

MCMole3D (Monte-Carlo MOlecular Line Emission) simulates the 3D molecular cloud emission in the Milky Way. In particular, it can simulate both the unpolarized and polarized emission coming from the first rotational line of Carbon Monoxide (CO, J=1-0). MCMole3D seeks to compare the simulated emission with that observed by full sky surveys from the Planck satellite.

FGCluster runs spectral clustering onto Healpix maps for parametric foreground removal, using a map encoding the feature to cluster as inputs. Pixel similarity is given by the geometrical affinity of each pixel in the sphere. FGCluster can also take an uncertainty map as an input, in which case the adjacency is modified in such a way that the pixel similarity accounts also for the statistical significance given by the pixel values in a map and the uncertainties.

SUSHI (Semi-blind Unmixing with Sparsity for hyperspectral images) performs non-stationary unmixing of hyperspectral images. The typical use case is to map the physical parameters such as temperature and redshift from a model with multiple components using data from hyperspectral images. Applying a spatial regularization provides more robust results on voxels with low signal to noise ratio. The code has been used on X-ray astronomy but the method can be applied to any integral field unit (IFU) data cubes.

UltraDark.jl simulates cosmological scalar fields. Written in Julia, it is inspired by PyUltraLight (ascl:1810.009) and designed to be simple to use and extend. It solves a non-interacting scalar field Gross-Pitaevskii equation coupled to Poisson's equation for gravitational potential. The scalar field describes scalar dark matter in models including ultralight dark matter, fuzzy dark matter, axion-like particles and the like. It also describes an inflaton field in the reheating epoch of the early universe.

Written in Python, DarsakX is used to design and analyze the imaging performance of a multi-shell X-ray telescope with an optical configuration similar to Wolter-1 optics for astronomical purposes. It can also assess the impact of figure error on the telescope's imaging performance and optimize the optical design to improve angular resolution for wide-field telescopes. By default, DarsakX uses DarpanX (ascl:2101.015) to calculate the mirror's reflectivity.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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).

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.

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).

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).

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.