GaMorNet is a Convolutional Neural Network to classify galaxies morphologically. GaMorNet does not need a large amount of training data (as it is trained on simulations and then transfer-learned on a small portion of real data) and can be applied on multiple datasets. Till now, GaMorNet has been tested on ~100,000 SDSS g-band galaxies and ~20,000 CANDELS H-band galaxies and has a misclassification rate of less than 5%

The Galaxy Morphology Posterior Estimation Network (GaMPEN) is a Bayesian machine learning framework that can estimate robust posteriors (i.e., values + uncertainties) for structural parameters of galaxies. GaMPEN also automatically crops input images to an optimal size before structural parameter estimation.

GaMPEN’s predicted posteriors are extremely well-calibrated (less than 5% deviation) and have been shown to be up to 60% more accurate compared to the uncertainties predicted by many light-profile fitting algorithms.

Once trained, it takes GaMPEN less than a millisecond to perform a single model evaluation on a CPU. Thus, GaMPEN’s posterior prediction capabilities are ready for large galaxy samples expected from upcoming large imaging surveys, such as Rubin-LSST, Euclid, and NGRST.

Spinifex is a pure Python tooling for ionospheric corrections in radio astronomy, e.g. getting total electron content and rotation measures.

StellarSpecModel is a Python package to interpolate the stellar spectral grid. Users provide stellar parameters (Teff, FeH, logg), the package will return the corresponding stellar spectrum.

This packagge also designed for generating and analyzing theoretical stellar spectral energy distributions (SEDs). The package includes functionality for both single and binary star systems, incorporating extinction models and the ability to handle photometric data in various filter bands.

Deep-Transit detects transits using a deep learning based 2D object detection algorithm. The code determines the light curve and outputs the transiting candidates' bounding boxes and confidence scores. It has been trained for Kepler and TESS data, and can be extended to other photometric surveys and even ground-based observations. Deep-Transit also provides an interface for training new datasets.

ROCKE-3D (Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics) models the atmospheres and oceans of solar system and exoplanetary terrestrial planets. Written in Fortran, it is a three-dimensional General Circulation Model (GCM). ROCKE-3D requires Panoply, the SOCRATES radiation code and spectral files, and has several additional dependencies.

The spectools_ir suite analyzes medium/high-resolution IR molecular astronomical spectra. It has three main sub-modules (flux_calculator, slabspec, and slab_fitter) and also offers a sub-module (utils) with a few additional functions. Written with infrared medium/high-resolution molecular spectroscopy in mind, spectools_ir generally assumes spectra are in units of Jy and microns and uses information from the HITRAN molecular database. Some routines are more general, but users interested in other applications should proceed with caution.

NbodyGradient computes gradients of N-body integrations for Newtonian gravity and arbitrary N-body hierarchies. Developed for transit-timing analyses and written in Julia, NbodyGradient gives derivatives of the transit times with respect to the initial conditions, either masses and Cartesian coordinates/velocities or orbital elements.

Hierarchical Semi-Sparse Cube (HiSS-Cube) framework provides highly parallel processing of combined multi-modal multi-dimensional big data. The package builds a database on top of the HDF5 framework which supports parallel queries. A database index on top of HDF5 can be easily constructed in parallel, and the code supports efficient multi-modal big data combinations. The performance of HiSS-Cube is bounded by the I/O bandwidth and I/O operations per second of the underlying parallel file system; it scales linearly with the number of I/O nodes and can be extended to any kind of multidimensional data combination and information retrieval.

Spectool is a toolkit designed for processing astronomical spectral data, offering a collection of common spectral analysis algorithms. The package includes functions for spectral resampling, spectral flattening, radial velocity measurements, spectral convolution broadening, and more. Each function in the package is implemented independently, allowing users to select and utilize the desired features as needed. The functions are designed with simple and intuitive interfaces, ensuring ease of use for various data sets and analysis tasks.

hmvec is a pure Python/numpy vectorized general halo model and HOD code. It includes support for 3d power spectra involving NFW, Battaglia electron density profiles and galaxy HODs. It also supports 2d power spectra including tSZ, cosmic shear, galaxy-galaxy lensing and CMB lensing. hmvec calculates a vectorized FFT for a given profile over all points in mass and redshift, using one double loop over mass and redshift to interpolate the profile Fourier transforms to the target wavenumbers; every other part of the code is vectorized.

SZiFi (pronounced "sci-fi") implements the iterative multi-frequency matched filter (iMMF) galaxy cluster finding method. It can be used to detect galaxy clusters with mm intensity maps through their thermal Sunyaev-Zeldovich (tSZ) signal. As a novel feature, SZiFi can perform foreground deprojection via a spectrally constrained MMF or sciMMF, and can also be used for point source detection.

cosmocnc evaluates the number count likelihood of galaxy cluster catalogs. Fast Fourier Transform (FFT) convolutions are used to evaluate some of the likelihood integrals. The code supports three types of likelihoods (unbinned, binned, and an extreme value likelihood); it also supports the addition of stacked cluster data (e.g., stacked lensing profiles), which is modeled in a consistent way with the cluster catalog. The package produce mass estimates for each cluster in the sample, which are derived assuming the hierarchical model that is used to model the mass observables, and generates synthetic cluster catalogs for a given observational set-up. cosmocnc interfaces with the Markov chain Monte Carlo (MCMC) code Cobaya (ascl:1910.019), allowing for easy-to-run MCMC parameter estimation.

Sledgehamr (ScaLar fiEld Dynamics Getting solvEd witH Adaptive Mesh Refinement) simulates the dynamics of coupled scalar fields on a 3-dimensional mesh. Adaptive mesh refinement (AMR) can boost performance if spatially localized regions of the scalar field require high resolution. sledgehamr is compatible with both GPU and CPU clusters, and, because it is AMReX-based (ascl:2409.012), offers a flexible and customizable framework. This framework enables various applications, such as the generation of gravitational wave spectra.

Based on oxkat (ascl:2009.003), polkat focuses on automating full polarization calibration and snapshot (i.e., second-scale) imaging of polarimetric radio data taken with the MeerKAT telescope. Accepting raw visibilities in Measurement Set format, polkat performs the necessary data editing, calibration (reference and self-calibration), and imaging to extract the complete polarization properties for user-defined target sources. Required software packages, including, but not limited to, CASA (ascl:1107.013), WSClean (ascl:1408.023), and QuartiCal (ascl:2305.006) are containerized with Apptainer/Singularity. polkat can be run locally or on high-performance computing that uses a slurm job scheduler; for the latter option, polkat will generate the necessary job submission files.

The Python code smhr (Spectroscopy Made Harder) wraps the MOOG spectral synthesis code (ascl:1202.009) to analyze high-resolution stellar spectra. It offers numerous analysis tools, including normalization of apertures, inverse variance-weighted stitching of overlapping apertures and/or sequential exposures. The code also provides Doppler measurement and correction, automatic measurement of EWs, and multiple methods for inferring stellar parameters; further, it measures elemental abundances from EWs or spectral synthesis and performs a rigorous uncertainty analysis. smhr can be run automatically (in batch mode) or interactively through a graphical user interface. Analyses can be saved to a single file for, for example, distribution to other spectroscopists or release with a publication.

legacypipe produces DESI Legacy Imaging Surveys (aka the Legacy Surveys). It can process individual exposures from many cameras, including the Dark Energy Camera on the Blanco telescope, the 90Prime camera on the Bok telescope, and the Mosaic3 camera on the Mayall telescope. The code can also process exposures from the Hyper-SuprimeCam on Subaru, the old SuprimeCam on Subaru, MegaCam on the Canada-France-Hawaii Telescope, and image products from the GALEX and WISE satellites. Legacypipe performs source detection, and then measurement via forward-modeling using The Tractor (ascl:1604.008). It generates coadded output images as well as catalogs, plus a variety of metrics useful for understanding the properties of the imaging.

Pigi (Parallel Interferometric GPU Imager) implements the image domain gridding algorithm and is compatible with both NVIDIA and AMD graphics cards. It provides a high-performance implementation capable of gridding hundreds of mega visibilities per second on modest hardware. The code can correct for baseline-, time-, and direction-dependent effects such as the primary beam or ionosphere as part of the (de)gridding process. Pigi provides end-to-end deconvolution capabilities with a basic iterative cleaning implementation.

THAI analyzes and visualizes climate model output for the TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI) project, which examines TRAPPIST-1e under several different atmosphere scenarios. The package includes functions to preprocess and clean the data and common and model-specific variables for convenience. THAI processes and plots the data, allowing for examination and intercomparison of results from the different models.

AccretR calculates mass, radius, and bulk composition along a specified growth track for orderly/hierarchical, runaway, and random particle accretion models. Elements in the model include concentrations of H, C, N, O, Na, Mg, Al, Si, S, Cl, K, Ca, and Fe. Maximal water is also computed, assuming all H goes into forming water. Accretional heat is also calculated. AccretR is optimized to build Jupiter's moon Europa, and Saturn's moons Titan and Enceladus, from CI, CM, CR, CK, CO and CV carbonaceous chondrite meteorites, cometary material (using comet 67P/Churyumov-Gerasimenko), and pure water ice.

Semi-automatic analysis for echelle spectra of stars.

The major parts are:
(1) full spectrum fit with a neural network emulator to estimate stellar parameters
(2) automatic continuum normalization with theoretical masks
(3) automatic equivalent width fits with theoretical masks
(4) ATLAS model atmosphere interpolation and equivalent width abundance determination using MOOG
(5) spectrum synthesis fitting using MOOG
(6) automatic abundance uncertainty analysis with error propagation and summary tables

LESSPayne can be run in a completely automatic mode, which is best used as a quick check of outputs during observing or an initial inspection. However, science-quality results still require a classic line-by-line analysis, where the quality of all fits is inspected by the user using the Spectroscopy Made Harder (smhr) graphical user interface or other automatic output plots. LESSPayne should be viewed as providing a high-quality initialization for an smhr file that reduces the time for a standard analysis.

If using LESSPayne, please cite Casey (2014) (https://ui.adsabs.harvard.edu/abs/2014PhDT.......394C/abstract), Ting et al. (2019) (https://ui.adsabs.harvard.edu/abs/2019ApJ...879...69T/abstract), and Ji et al. (2020) (https://ui.adsabs.harvard.edu/abs/2020AJ....160..181/abstract) in addition to this ASCL entry.

Additionally as always, please cite the model atmospheres used (default is ATLAS, https://ui.adsabs.harvard.edu/abs/2003IAUS..210P.A20C/abstract), radiative transfer code (default is MOOG including scattering, https://ui.adsabs.harvard.edu/abs/1973PhDT.......180S/abstract, https://ui.adsabs.harvard.edu/abs/2011AJ....141..175S/abstract, https://ui.adsabs.harvard.edu/abs/2012ascl.soft02009S/abstract), and atomic data (if using any built into this package, see references in https://ascl.net/2104.027 and https://ui.adsabs.harvard.edu/abs/2021RNAAS...5...92P/abstract).

IGRINS RV extracts radial velocities (RVs) from spectra taken with the Immersion GRating INfrared Spectrometer (IGRINS). It uses a modified forward modeling technique that leverages telluric absorption lines as a common-path wavelength calibrator. IGRINS RV achieves an RV precision in the H and K bands of around 25-30 m/s for narrow-line stars.

The IGRINS (Immersion Grating Infrared Spectrometer) PipeLine Package (PLP) processes all IGRINS observing data, such as that from the McDonald 2.7m, LDT/DCT, or Gemini-South telescopes, without (or with a minimum of) human interaction. It was also designed to be adaptable for a real time processing during the observing run. The IGRINS PLP uses a "recipe" to process a certain data group and requires an input file describing which recipe should be used with which data sets.

TESS-SIP creates a Systematics-insensitive Periodogram (SIP) using lightkurve (ascl:1812.013) to detect long period rotation in NASA's TESS mission data. The SIP method detrends telescope systematics (the TESS scattered light) simultaneously with calculating a Lomb-Scargle periodogram, thus allowing estimation of the rotation rate of variables with a period of >30 days when there are multiple sectors.

blasé performs whole-spectrum fitting by cloning 10,000+ spectral lines from a pre-computed synthetic spectral model template and then learning the perturbations to those lines through comparison to real data. Each spectral line has four parameters, yielding possibly 40,000+ parameters. The technique uses autodiff to tune the parameters precisely and quickly. Built in PyTorch with native GPU support, blasé can be extended to, for example, Doppler imaging, Precision RVs, and abundances.

ATOCA (Algorithm to Treat Order Contamination) extracts and decontaminates spectroscopic images with multiple sources or diffraction orders. For all orders and sources, the package takes the wavelength solutions, the trace profiles, the throughputs, and the spectral resolution kernels as input. From these, ATOCA simultaneously models the detector and extracts the spectra.

Optimal BLS explicitly includes Keplerian dynamics in transit searches, which enhances transit detectability while reducing the resources and time usually required for such searches. The (standard) BLS is either fairly insensitive to long-period planets or less sensitive to short-period planets and computationally slower by a significant factor of ~330 (for a 3 yr long dataset). Physical system parameters, such as the host star's size and mass, directly affect transit search. Optimal BLS leverages this understanding to optimize the search for every star individually.

spaceKLIP reduces and analyzes JWST NIRCam and MIRI coronagraphy data. The package runs the official JWST stage 1 and 2 data reduction pipelines with several modifications that improve the quality of high-contrast imaging reductions. spaceKLIP then performs PSF subtraction based on the KLIP algorithm as implemented in pyKLIP (ascl:1506.001), outputs contrast curves, and enables forward model PSF fitting for any detected companions in order to extract their properties (offset and flux).

SpecMatch-Emp extracts the fundamental properties of a star (effective temperature, radius, and metallicity) by comparing a target star's spectrum to a library of spectra from stars with known properties. The spectral library comprises high-resolution, high signal-to-noise observed spectra from Keck/HIRES for 404 touchstone stars with well-determined stellar parameters derived from interferometry, asteroseismology, and spectrophotometry. The code achieves accuracies of 100K, 15%, and 0.09 dex in Teff, Rstar, and [Fe/H] respectively for FGKM dwarfs.

The PACMAN pipeline reduces and analyzes Hubble/Wide Field Camera 3 (WFC3) observations of transiting exoplanets. The pipeline runs end-to-end, beginning with a time series of 2D images and ending with a spectrum for the planet, and includes both spectral extraction and light curve fitting. PACMAN can easily fit multiple observations simultaneously.

PyMieScatt (Python Mie Scattering) calculates relevant parameters including absorption, scattering, extinction, asymmetry, and backscatter. The package also contains single-line functions to calculate optical coefficients (in Mm-1) of ensembles of particles in lognormal (with single or multiple modes) or custom size distributions. The inverse calculations retrieve the complex refractive index from laboratory measurements of scattering and absorption (or backscatter), useful for studying atmospheric organic aerosol of unknown composition.

Crimson Light is a tool to visualize and slice metadata on the available archival observations of samples of astrophysical objects. This visualization enables the user to view available multi-wavelength datasets for a range of objects, optionally filtering the displayed observations on the basis of (angular) resolution, wavelength/frequency coverage, and other properties.

WATSON (Visual Vetting and Analysis of Transits from Space ObservatioNs) enables a comfortable visual vetting of transiting signal candidates from Kepler, K2, and TESS missions. The code looks for transit-like signals that could be generated by other sources or instrument artifacts and runs simplified tests on scenarios including transit shape model fit, odd-even transits checks, and centroids shifts. It also considers optical ghost effects, transit source offsets, and several other scenarios. WATSON then computes metrics and flags problematic signals.

The Tiberius pipeline, written in Python, extracts and reduces time-series spectra and fits exoplanet transit light curves. Written in Python, the code can extract spectra from all four JWST instruments, ground-based long-slit spectrographs, and Keck/NIRSPEC echelle spectra. The light curve fitting routines in Tiberius can be used as standalone code to fit, for example, HST light curves extracted with other methods.

SPCA (Spitzer Phase Curve Analysis) analyzes Spitzer/IRAC observations of exoplanets. It implements 2D polynomial, Pixel Level Decorrelation, BiLinearly-Interpolated Sub-pixel Sensitivity mapping, and Gaussian Process decorrelation methods, allowing the user to change techniques by setting a single variable. The code's modular structure enables integration of custom astrophysical models and decorrelation methods. SPCA can reduce and decorrelate multiple datasets with a single command.

TLCM (Transit and Light Curve Modeler) analyzes the light curves of transiting exoplanets. Written in IDL and runnable under GDL, the code fits the light curves with quadratic limb darkening law; the limb darkening coefficients can be different for the two objects considered. The package carries out the fit of the transit + occultation + out-of-transit variation + radial velocity (RV) model to the observed light curve to find the best agreement between model and observations. TLCM also estimates the uncertainties of the fitted parameters.

The Giants pipeline accesses TESS data, produces noise-corrected light curves, and searches for planets transiting evolved stars. Built with Lightkurve (ascl:1812.013) and written in Python, its emphasis is on finding giant planets around subgiant and RGB stars in TESS Full Frame Images (FFIs). Giants produces a one-page PDF summary for each target.

polyrot computes the structure of rotating polytropic bodies. The code computes the equilibrium structure of rotating planets and stars modeled as "polytropes" with pressure and density, and can also compute models including rotation specified as a function of cylindrical radius. polyrot includes a basic plotting function that can show a cross-section along the rotation axis with the colormap indicating density, and a line plotting the surface radius of the star; these and other quantities are attached as attributes to the model.

MOLPOP-CEP calculates the exact solution of radiative transfer problems in multi-level atomic systems. The radiative transfer equations are analytically integrated to reduce the final problem to the solution of a non-linear algebraic system of equations in the level populations. The code uses Coupled Escape Probability formalism to analytically solve the radiative transfer. Written in Fortran 90, MOLPOP-CEP is limited to plane-parallel slabs that can present arbitrary spatial variations of the physical conditions.

chemcomp models and enables the study of the formation of planets in 1D protoplanetary disks. It includes disk physics for viscous disk evolution, pebble growth and evolution applying the two populations model, evaporation and condensation at evaporation lines, and chemical compositions. Written in Python, chemcomp also includes planet physics for type-I and type-II migration, thermal and dynamical torques, and pebble and gas accretion.

speedyfit fits the photometric spectral energy distribution of stars using a Markov chain Monte Carlo approach to determine the errors on the derived parameters. This command line tool searches the most common online databases for photometric observations of a target and automatically pulls archive photometry from the main surveys. The code fits theoretical atmosphere models to the obtained photometry. Speedyfit handles both single and binary stars and allows for the inclusion of constraints from other sources, such as atmosphere parameters derived from spectroscopy, distances, or reddening.

RadioBEAR (Radio BErkeley Atmospheric Radiative-transfer) calculates the brightness temperature of planetary atmospheres in the meter-to-millimeter wavelength range. The code assumes the atmosphere is in local thermodynamic equilibrium; it can calculate the RT-derived brightness temperatures of a planet on each location on the planet and create 2D model maps of the planet's disk.

SpectralRadex runs RADEX (ascl:1010.075) directly from Python and creates model spectra from RADEX outputs. The package uses F2PY (Fortran to Python interface generator) to compile a version of RADEX written in modern Fortran, most importantly dropping the use of common blocks. As a result, running a RADEX model creates no subprocesses and can be parallelized. SpectralRadex uses the RADEX calculated line opacities and excitation temperatures to calculate the brightness temperature as a function of frequency. This allows observed spectra to be modeled in Python in a non-LTE fashion.

breads (Broad Repository for Exoplanet Analysis, Discovery, and Spectroscopy) provides a toolkit for data analyses in astronomical spectroscopy of exoplanets, in particular frameworks for rigorous forward modeling of observational data to achieve physical inferences with reduced systematic biases. Users choose a data class, a forward model function, and a fitting strategy. Data classes normalize the data format, simplifying reduction across different spectrographs while allowing for specific behaviors of each instrument to also be coded into their own specific class. breads provides specific functionality for modeling data from JWST NIRSpec, Keck OSIRIS, and Keck KPIC, but the underlying mathematical framework is more general.

ECCOplanets simulates the formation of rocky planets in chemical equilibrium (based on a Gibbs free energy minimisation). The package includes tools for analyzing the simulated planet and two databases, one of thermochemical data and the other of stellar abundance patterns. ECCOplanets provides a simplified starting point for getting an approximate idea of the variety of planetary compositions based on the variety of stellar compositions.

NEXO (Nonsingular Estimator for EXoplanet Orbits) fits exoplanet orbits to direct astrometric measurements using nonlinear batch estimation and nonsingular orbital elements. The estimation technique is based on the unscented transform, which approximates probability distributions using finite, deterministic sets of weighted sample points. Furthermore, NEXO uses Gaussian mixtures to account for the strong nonlinearities in the measurement model. As a fitting basis, it uses a set of orbital elements developed specifically for directly observed exoplanets, combining features of the Thiele–Innes constants and the Cohen–Hubbard nonsingular elements.

ExoTR (Exoplanetary Transmission Retrieval) interprets exoplanetary transmission spectra using a Bayesian inverse retrieval algorithm. The code can be used in two ways; the first is by leveraging the physics forward model only to generate synthetic planetary atmospheric transmission spectra (including the addition of errorbars). The second way is by using a retrieval routine based on nested sampling (i.e., MultiNest (ascl:1109.006)) to extract physical and chemical information from the input transmission spectra.

The CIANNA framework creates and trains deep-learning models for astronomical data analysis. Functionalities and optimizations are added based on relevance to astrophysical problem-solving. CIANNA builds and trains a wide variety of neural network architectures for various tasks through a high-level Python interface. It supports both computing on CPU and GPU acceleration through low-level CUDA programming, taking advantage of AI-dedicated hardware substructures. CIANNA distinguishes itself by its low latency, allowing tight integration with other codes.

tshirt (Time Series Helper and Integration Reduction Tool) processes raw data on exoplanet systems for time series science. It reduces raw data to produce flat fields, subtracts bias, and corrects gain. tshirt also performs photometric and optimal spectral extraction of light curves.

Haystacks creates high-fidelity spatial and spectral models of complete planetary systems including star, planets, interplanetary dust, and astrophysical background sources. These models are intended for use in simulations of direct imaging and spectroscopy with high-contrast instruments on exoplanet missions to prepare future exoEarth observations.

pympc performs checks for the presence of minor and major Solar System bodies at specified coordinates. Orbital elements from the Minor Planet Center are used to propagate orbits to determine the position of asteroids, comets, NEOS, planets and major moons at the request epoch. Topocentric corrections are included to allow for observatory-specific positions. The requested position can also be checked for being within the Hill Sphere (in projection) of any Solar System planet.

CAFE (Continuum And Feature Extraction) fits JWST IFU data; the code is a Python version of the original CAFE IDL software for fitting Spitzer/IRS spectra. The code contains two main tools: (1) the CAFE Region Extraction Tool Automaton (CRETA) and (2) the CAFE spectral fitting tool, or fitter. CRETA performs single-position and full-grid extractions from JWST IFU datasets; that is, from pipeline-processed cubes obtained with the NIRSpec IFU and MIRI MRS instruments. The CAFE fitter uses the spectra extracted by CRETA (or spectra provided by the user) and performs a spectral decomposition of the continuum emission (stellar and/or dust), as well as of a variety of common spectral features (in emission and absorption) present in the near- and mid-IR spectra of galaxies, including prominent, broad emission from small grains and molecules such as Polycyclic Aromatic Hydrocarbons (PAHs). The full dust treatment (size and composition) performed by CAFE allows the dust continuum model components to fit not only spectra from typical star-forming galaxies, but also those from more extreme, heavily dust-obscured starburst galaxies, such as luminous infrared galaxies (LIRGs and ULIRGs), active galactic nuclei (AGN), or very luminous quasars.

S3Fit is a python code for the analysis of observational data of galaxies, which can fit spectrum and multi-band photometric Spectral Energy Distribution (SED) simultaneously. It is written to improve the moderate constraints on properties of continuum models in a pure spectral fitting due to the limited wavelength coverage. S3Fit support multiple models with multiple components, and can handle complex systems with a mixed contribution of Active Galactic Nucleus (AGN) and its host galaxy in both of continua and emission lines (e.g., narrow lines and broad outflow lines). The fitting strategy is optimized to enable an efficient solution of the best-fit results for several tens of parameters and model components. S3Fit is also extensible for adding new functions and components by users (e.g., new band filters, new star formation history functions, new emission lines, and also new types of models).

A tabulated version of the slim disk model for fitting tidal disruption events (TDEs) is presented. The synthetic X-ray spectral library is created by ray-tracing stationary, general relativistic slim disks and consistently incorporating gravitational redshift, Doppler, and lensing effects.

easyspec is a tool designed to streamline long-slit spectroscopy, offering an intuitive framework for reducing, extracting, and analyzing astrophysical spectra.

γ-Cascade (also called GCascade) uses a semi-analytic approach to model gamma-ray propagation through cosmological distances accounting for attenuation, the formation of electromagnetic cascades,and cosmological redshifting. V4 implements an assortment of the most widely used EBL models, significantly improves computational precision, and provides new core functionality. Additionally, GCascadeV4 uses a new method to estimate the uncertainty due to the EBL model.

lintsampler performs linear interpolant sampling to create a set of sample points from a density function. The code uses the evaluation of the density at the two endpoints of 1D interval, or the four corners of a 2D rectangle, or generally the 2^k vertices of a dimensional hyperbox (or a series of such hyperboxes, e.g., the cells of a k-dimensional grid) to draw random samples within the hyperbox. lintsampler works by evaluating a given PDF on the nodes of a grid (or grid-like structure, such as a tree); the number of evaluations (and memory occupancy) grows exponentially with the number of dimensions.

POSEIDON models and retrieves 1D, 2D, and 3D exoplanet transmission spectra. Given a set of observed exoplanet spectra from space-based or ground-based telescopes, the code uses Bayesian techniques to infer the atmospheric properties of the planet. POSEIDON also includes disk-integrated thermal emission and reflection spectra modeling and retrievals for both secondary eclipses and directly-imaged substellar objects.

Mister plotter (mr-plotter) creates paper-quality mass-radius diagrams based on a wide range of state-of-the-art models of planetary interiors and atmospheres. It can be used to contextualize planets and infer their possible internal structures. It can also be used to search for correlations at a population level with its color-coding option based on any property collected in the NASA Exoplanet Archive, PlanetS, and Exoplanet.eu catalogs. mr-plotter can also produce article-ready two-column plots.

The visualization tool MARDIGRAS (Mass-Radius DIaGRAm with Sliders) enables simple and intuitive manipulation of mass-radius relationships (also known as iso-composition curves) using interactive sliders. It infers composition based on mass and radius (and other parameters). As a result, it requires use of actual measurements of mass and radius; values that are upper/lower limits, derived from empirical mass-radius relations, or are somewhat controversial should not be used. MARDIGRAS screen captures can be used for general scientific communication but are not of suitable quality for article publication.

The euclidlib python package is an unofficial tool designed to read products from the Euclid Consortium Science Ground Segment. Euclidlib offers user-friendly reading and writing routines, and effectively enables to work overall with Large-Scale Structure cosmological products.

squishyplanet produces realistic lightcurves and phase curves of non-spherical exoplanets. The code generates models of triaxial planets; fitting for the triaxial shape can provide additional constraints on the planet’s interior properties and evolution. squishyplanet also handles complex limb darkening profiles while also accounting for the planet’s non-circular, potentially time-varying, projected shape.

CLOWN (CLOud Watcher at Night) detects and monitors clouds in real time. The software can be used with any type of all-sky camera even without knowing its parameters; parameters are stored instead in a configuration file. CLOWN correctly traces cloud positions in the sky and provides accurate pointing information to the observation planning of the optical telescope to avoid cloudy areas.

cogsworth merges rapid population synthesis and galactic dynamics together; the code can evolve a population of stars using population synthesis while self-consistently integrating their orbits with a chosen galactic potential. This enables exploration of the full evolutionary history (both stellar and orbital) of a population of stars and the ability to make predictions for present day kinematics and other distributions. cogsworth also provides tools for transforming the intrinsic populations into observables and for classifying the nature of each system.

ForestFlow emulates the linear biases and small-scale deviation parameters of the 3D flux power spectrum of the Lyman-alpha forest. The parameters are modeled as a function of cosmology – the small-scale amplitude and slope of the linear power spectrum – and the physics of the intergalactic medium.

BlendingToolKit (BTK) generates images of blended objects and evaluate performance metrics on various detection, deblending and measurement algorithms. The toolkit is a convenient way to produce multi-band postage stamp images of blend scenes and evaluate the performance of deblending algorithms, as well as train samples for machine learning algorithms.

Combustion Toolbox (CT) models thermodynamic properties of the gaseous species with the ideal gas equation of state (EoS). Written in MATLAB, this thermochemical code is modular and has three main modules: CT-EQUIL, CT-SD, and CT-ROCKET. CT-EQUIL computes the composition at the equilibrium of multi-component gas mixtures that undergo canonical thermochemical transformations from an initial state (reactants). CT-SD solves steady-state shock and detonation waves in either normal or oblique incidence, and CT-ROCKET computes the theoretical performance of rocket engines under highly idealized conditions. Modules can be accessed through user-friendly GUI or from MATLAB’s command line in plain code mode.

FitTeD solves time-dependent general relativistic disc equations to fit multi-band light curves and spectra. It includes relativistic optics effects such as Doppler and gravitational energy shifting, and gravitational lensing, and can include non-disc light curve and spectral components to, for example, model the early time rise and decay of tidal disruption event light curves in optical-to-UV bands. FitTeD also provides Monte Carlo Markov Chain fitting procedures that return posterior distributions of black hole and disc parameters.

gwforge generates mock gravitational wave detector data using user-defined population and arbitrary detector sensitivity. The code can, for example, simulate a wide range of binary source populations by specifying parameters such as the local merger rate, distribution functions, and additional keyword arguments, and simulate coloured Gaussian or zero noise using a provided or default power spectrum to represent the detector noise. gwforge can also inject gravitational wave signal(s) into the generated detector data using the previously generated population and a chosen waveform model.

Particle_spray models the position and velocity distributions of newly-escaped stream particles that emerge from globular clusters (GCs). Rather than computing the detailed internal cluster dynamics, which is computationally expensive, the code directly draws tracer particles from these distributions. This algorithm is fast and accurate, and is implemented in a series of notebooks for several galactic dynamics codes, including AGAMA (ascl:1805.008) and galpy (ascl:1411.008).

WD_models transforms white dwarf (WD) photometry to physical parameters (i.e., mass, cooling age, and Teff) and vice versa, based on interpolation of existing WD atmosphere grid and cooling models. The code converts the coordinates of Gaia (and other passbands) H--R diagram into WD parameters and plots contours of WD parameters on the Gaia (and other passbands) H--R diagram. WD_models also provides tools to transform any desired WD parameters and compare the results of different WD models. In addition, the user may customize many parameters, such as the choice of cooling models and setting details of plotting.

The Payne precisely and simultaneously determines numerous stellar labels from observed spectra based on fitting physical spectral models. It fits all all labels (stellar parameters and element abundances) simultaneously, and uses spectral models where the atmosphere structure and the radiative transport are consistently calculated to reflect the stellar labels. The Payne leads to both precise and accurate estimates of stellar labels, based on physical models and without re-calibration.

Siril reduces reduction and improves the signal/noise ratio of an image from multiple captures. It can can align automatically or manually, and stack and enhance pictures from various file formats, even image sequence files (films and SER files). Its Graphical User Interface (GUI) allows manual processing of images in addition to scripts or typing commands. Siril provides astrometry and photometry options and performs geometric transformations in addition to many other tools.

Spectuner identifies spectral lines of interstellar molecules automatically. The code uses XCLASS (ascl:1810.016) for the spectral line model and SciPy for the peak finder. Spectral fitting is performed using article swarm optimization and the peak matching loss function. From frequency in a unit of MHz and temperature in a unit of K, Spectuner returns the combined spectrum, identification of the combined spectrum, and the identification of all candidates.

CosmoFlow automatically computes cosmological correlators. The Cosmological Flow approach is based on computing cosmological correlators by solving differential equations in time governing their time evolution through the entirety of the spacetime during inflation, from their origin as quantum fluctuations in the deep past to the end of inflation. This method takes into account all physical effects at tree-level without approximation. Specifically, CosmoFlow computes the two- and three-point correlators of fields and/or conjugate momenta X a in Fourier space that includes an arbitrary number of degrees of freedom with any propagation speeds, couplings, and time-dependencies.

DIES calculates equilibrium dust temperatures and the resulting dust emission spectra. It handles spherical models (cells are spherical shells), computes dust temperatures (equilibrium temperatures only), and returns spectra for different impact parameters. The code uses the immediate re-emission method; it is not suitable for problems where the stochastic heating of the grains is important. DIES can assume constant dust properties throughout the model, and also offers an alternative script that allows dust properties to be set cell by cell. The program uses OpenCL libraries and is recommended to be run on GPUs.

exoTEDRF (Exoplanet Transit and Eclipse Data Reduction Framework) reduces and analyzes JWST exoplanet time series observations. The code is modular and tunable, which makes it easy to run multiple reductions of a given dataset, and therefore ascertain whether the spectral features driving atmosphere inferences are robust or are sensitive to the peculiarities of a given reduction. exoTEDRF has full support for TSOs with NIRISS/SOSS and can run the ATOCA extraction algorithm (ascl:2502.016) to explicitly model the SOSS order overlap.

Codex Africanus presents radio astronomy algorithms to the user as modular functions accepting NumPy inputs and producing NumPy outputs. Internally, it uses Numba to accelerate these codes and Dask to parallelize and distribute them. The library contains functions for plotting convolution filters and tapers associated with convolution filters and can compute the discretised direct Fourier transform (DFT) for an ideal interferometer. Codex Africanus has routines for gridding or degridding complex visibilities onto or from an image, includes deconvolution algorithms and coordinate transforms, and many other functions.

nifty-ls evaluates the Lomb-Scargle periodogram very quickly and accurately. The Lomb-Scargle periodogram, used for identifying periodicity in irregularly-spaced observations, is useful but computationally expensive. However, when it is phrased mathematically as a pair of non-uniform FFTs (NUFFTs), FINUFFT (ascl:2412.007), which is really fast, can be leveraged to improve performance. It also enables GPU (CUDA) support and is several orders of magnitude more accurate than Astropy's (ascl:1304.002) Lomb Scargle with default settings.

FINUFFT (Flatiron Institute Nonuniform Fast Fourier Transform) computes the three standard types of nonuniform FFT to a specified precision, in one, two, or three dimensions. It can be run on a multi-core shared-memory machine or on a GPU. It is extremely fast and has very simple interfaces to most major numerical languages (such as C/C++, Fortran, MATLAB, octave, Python, and Julia). FINUFFT also provides more advanced (vectorized and “guru”) interfaces that allow multiple strength vectors and the reuse of FFT plans.

Coport computes covariant polarized radiation transfer in any spacetime. It is particularly useful for imaging black hole accretion systems. Written in Julia, it contains functions for handling the computation of all rays and a single ray, and deriving initial ray directions. Coport also has functions for interpolating GRMHD data, obtaining covariant emission, absorption, and Faraday rotation coefficients, and projecting the polarization tensor at the observer's screen, among other tasks.

pmwd simulates and models cosmological evolutionary history. The code includes reverse time integration in addition to traditional forward simulation, enabling symmetrical dynamics analysis using the adjoint method. The pmwd particle-mesh model supports fully-differentiable analytic, semi-analytics, and deep learning components in parallel. Based on JAX (ascl:2111.002), pmwd is optimized for PU computation.

BADASS (Bayesian AGN Decomposition Analysis for SDSS Spectra) decomposes Sloan Digital Sky Survey (SDSS) spectra and fits Type 1 ("broad line") Active Galactic Nuclei (AGN) in the optical. The fitting process uses the Bayesian affine-invariant Markov-Chain Monte Carlo sampler emcee (ascl:1303.002) for robust parameter and uncertainty estimation, as well as autocorrelation analysis to access parameter chain convergence. Out of the box, BADASS fits SDSS spectra, and MANGA IFU cube data; the code can be modified to fit user-input spectra of any instrument.

dask-ms constructs xarray datasets from CASA tables, thus providing a data access layer for Measurement Set v2.0 data. It supports the CASA Data Table System, Zarr and Apache Arrow formats, but abstracts them away from the developer at the xarray dataset level. It therefore serves as a basis for writing distributed PyData Radio Astronomy applications and supports writing variables back to the respective column in the Table. The intention behind dask-ms is to support the Measurement Set as a data source and sink for the purposes of writing parallel, distributed radio astronomy algorithms.

Stimela2 develops data reduction workflows and is a significant update of Stimela (ascl:2305.007). Though designed for radio astronomy data, it can be adapted for other data processing applications. Stimela2 represents workflows by linear, concise and intuitive YAML-format "recipes". Atomic data reduction tasks (binary executables, Python functions and code, and CASA tasks) are described by YAML-format "cab definitions" detailing each task's "schema" (inputs and outputs). Stimela2 provides a rich syntax for chaining tasks together, and encourages a high degree of modularity: recipes may be nested into other recipes, and configuration is cleanly separated from recipe logic. Tasks can be executed natively or in isolated environments using containerization technologies such as Apptainer. Stimela2 facilitates the deployment of scalable, distributed workflows by interfacing with the Slurm scheduler and the Kubernetes API, the latter allowing workflows to be readily deployed in the cloud.

Twinkle calculates and plots the stellar spectral energy distribution (SED) using empirical photometric data and stellar model grids. The code was originally created to help calculate the excess infrared (IR) flux from a star; the presence of an IR excess indicates dust orbiting the star. This dust likely results from the grinding and collisions of asteroids, influenced by a larger planetary object—pointing to the potential for finding planets. Twinkle quickly calculates the temperature and location of the dust to first order by fitting the assumed blackbody or modified blackbody function to the broadband excess emission.

Colume (COLUMn to vOLUME) uses the statistical and spatial distribution of a column density map to infer a likely volume density distribution along each line of sight. This Python package incorporates all pre-processing (in particular re-sampling) functions needed to efficiently work on the column density maps. Colume's outputs are saved in Numpy format.

This project presents a comprehensive spectroscopic analysis of O and B-type stars, neutron stars, and white dwarfs, with a focus on the detection of helium (He) and oxygen (O) in stellar atmospheres. By leveraging data from the Sloan Digital Sky Survey (SDSS) and utilizing tools such as Astropy, Astroquery, and Specutils, the project aims to identify key spectral lines of helium and oxygen, as well as the formation of heliox (OHe) molecules. The methodology involves querying SDSS for relevant spectral data, filtering and analyzing it based on stellar classification, and visualizing the results using advanced techniques. The findings contribute to the understanding of stellar evolution, chemical processes, and the role of these elements in various stellar classes. Additionally, the project incorporates interactive data exploration with Aladin Lite and Simbad, offering a robust framework for future astrophysical research.

This notebook provides a comprehensive approach for analyzing and visualizing astronomical data from FITS (Flexible Image Transport System) files, focusing on moment maps derived from molecular line emissions within the galaxy NGC 0628. The analysis involves applying various image processing techniques to handle corrupted pixels, reconstruct images, and enhance the quality of moment maps. The notebook also demonstrates how to simulate super-resolution to improve the spatial resolution of the data. By utilizing Gaussian filtering, median filtering, and contrast enhancement, the approach improves the clarity and precision of the data, making it suitable for detailed astrophysical studies. This tool serves as an efficient method for processing and visualizing large-scale astronomical datasets for further analysis and scientific interpretation.

NEMESISPY infers the atmospheric properties of exoplanets, such as chemical composition, using spectroscopic data. The package calculates radiative transfer using the correlated-k approximation and for parametric atmospheric modelling. NEMESISPY is a Python implementation of the well-established Fortran NEMESIS library (ascl:2210.009), which has been applied to the atmospheric retrievals of both solar system planets and exoplanets employing numerous different observing geometries.

IcyDwarf calculates the coupled physical-chemical evolution of an icy dwarf planet or moon. The code calculates the thermal evolution of an icy planetary body (moon or dwarf planet), with no chemistry, but with rock hydration, dehydration, hydrothermal circulation, core cracking, tidal heating, and porosity; the depth of cracking and a bulk water:rock ratio by mass in the rocky core are also computed. It also calculates whether cryovolcanism is possible by the exsolution of volatiles from cryolavas. IcyDwarf also determines the equilibrium fluid and rock chemistries resulting from water-rock interaction in subsurface oceans in contact with a rocky core, up to 200ºC and 1000 bar.

SMINT (Structure Model INTerpolator) obtains posterior distributions on the H/He or H2O mass fraction of a planet; its interface is user-friendly. The parameters of the planet of interest are input with specifications on the priors that should be used. SMINT returns publication-ready plots presenting the joint parameters constraints obtained from interpolating the interior models grid of interest as well as confidence intervals for each parameter.

DarkMatters calculates multi-frequency and multi-messenger emissions from WIMP annihilation and decay. This can be done both for standard channels and custom models, with the ability to produce surface brightnesses and integrated fluxes as well as maps in FITS format to compare to actual data. DarkMatters uses an accelerated ADI solver such as GALPROP (ascl:1010.028) for electron diffusion with an innovative sparse matrix approach. Additionally, there is the option to use a Green's function approximate solution (implemented in both C++ and Python).

The numerical modeling code DustPOL-py calculates the multi-wavelength polarization degree of absorption and thermal dust emission based on Radiative Torque alignment (RAT-A), Magnetically enhanced RAT (MRAT) and Radiative Torque Disruption (RAT-D). The code saves the output files (wavelength and degree of polarization) for further analysis and is idealization for diffuse ISM, molecular clouds and star-forming regions; it also predicts the polarization spectrum for one- or two-dust layers. A web-interface GUI for DustPOL-py is also available.

DArk Matter SPIkes (DAMSPI) analyzes dark matter spikes around Intermediate Mass Black Holes (IMBHs) in the Milky Way. It extracts an IMBH catalog with the corresponding dark matter spike parameters from EAGLE simulations to probe a potential gamma-ray signal from dark matter self-annihilation. The catalog includes, among others, the coordinates, mass, formation redshift, and spike parameters for each individual IMBH.

jaxspec performs statistical inference on X-ray spectra. It loads an X-ray spectrum (in the OGIP standard), defines a spectral model from the implemented components, and calculates the best parameters using state-of-the-art Bayesian approaches. The code is built on top of JAX (ascl:2111.002) to provide just-in-time compilation and automatic differentiation of the spectral models, enabling the use of sampling algorithms. jaxspec is written in pure Python and is not dependent on HEASoft (ascl:1408.004).

mochi_class extends the hi_class code (ascl:1808.010), itself a patch to the Einstein-Boltzmann solver CLASS (ascl:1106.020). It replaces α-functions by stable basis to ensure stability and takes general functions of time as input, including the dark energy equation of state or its normalized background energy-density. mochi_class provides stability test checking for mathematical (classical) instabilities in the scalar field fluctuations, and also includes a GR approximation scheme, among other new capabilities.

HIILines analytically models lines emitted by the ionized interstellar medium (ISM). It covers [OIII], [OII], H_α, and H_β lines. The strength of HIILines is its high computational efficiency. It can be used for galaxy spectroscopic survey measurement interpolations assuming a one-zone picture and galaxy line emission measurement design and forecasts. HIILines also performs post-processing of hydrodynamical galaxy formation simulations for ISM emission lines.

McFine performs complex, multi-component hyperfine spectra fitting in astronomical data. It turns line intensities into gas conditions using a fully automated Bayesian method. Written in Python, the code uses Markov chain Monte Carlo (MCMC) to characterize model denegeracies. It handles local thermodynamic equilibrium (LTE) and radiative-transfer (RT) models and can fit individual spectra and data cubes; given a data cube, it can also use the neighboring information to attempt a better fit. McFine also fits the minimum number of distinct components to avoid overfitting.

The spectral classification code Diagnose assigns one of four classifications (star, galaxy, quasar, or unknown) to each source and returns a redshift estimate for the galaxies and quasars and a velocity estimate for the stars. The code uses a chi-squared minimization for linear combinations of principal component templates to determine a best-fit spectral classification and redshift estimate. It computes three best-fit chi-squared values: one for stellar type and velocity, one for galaxy type and redshift, and one for a quasar and redshift. Diagnose then compares the best fit of these three reduced chi-squared values to the second best fit and evaluates the difference against a statistical threshold.