Autoastrom performs automated astrometric corrections on an astronomical image by automatically detecting objects in the frame, retrieving a reference catalogue, cross correlating the catalog with CCDPACK (ascl:1403.021) or MATCH, and using the ASTROM (ascl:1406.008) application to calculate a correction. It is distributed as part of the Starlink software collection (ascl:1110.012).

AutoBayes automatically generates customized algorithms from compact, declarative specifications in the data analysis domain, taking a statistical model as input and creating documented and optimized C/C++ code. The synthesis process uses Bayesian networks to enable problem decompositions and guide the algorithm derivation. Program schemas encapsulate advanced algorithms and data structures, and a symbolic-algebraic system finds closed-form solutions for problems and emerging subproblems. AutoBayes has been used to analyze planetary nebulae images taken by the Hubble Space Telescope, and can be applied to other scientific data analysis tasks.

Automark models photon counts collected form observation of variable-intensity astronomical sources. It aims to mark the abrupt changes in the corresponding wavelength distribution of the emission automatically. In the underlying methodology, change points are embedded into a marked Poisson process, where photon wavelengths are regarded as marks and both the Poisson intensity parameter and the distribution of the marks are allowed to change.

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.

AutoProf performs basic and advanced non-parametric galaxy image analysis. The pipeline's design allows for fast startup and easy implementation; the package offers a suite of robust default and optional tools for surface brightness profile extractions and related methods. AUTOPROF is highly extensible and can be adapted for a variety of applications, providing flexibility for exploring new ideas and supporting advanced users.

AutoSourceID-Light (ASID-L) analyzes optical imaging data using computer vision techniques that can naturally deal with large amounts of data. The framework rapidly and reliably localizes sources in optical images.

AUTOSPEC provides fast, automated extraction of high quality 1D spectra from astronomical datacubes with minimal user effort. AutoSpec takes an integral field unit (IFU) datacube and a simple parameter file in order to extract a 1D spectra for each object in a supplied catalogue. A custom designed cross-correlation algorithm improves signal to noise as well as isolates sources from neighboring contaminants.

AUTOSTRUCTURE calculates atomic and ionic energy levels, radiative rates, autoionization rates, photoionization cross sections, plane-wave Born and distorted-wave excitation cross sections in LS- and intermediate-coupling using non- or (kappa-averaged) relativistic wavefunctions. These can then be further processed to form Auger yields, fluorescence yields, partial and total dielectronic and radiative recombination cross sections and rate coefficients, photoabsorption cross sections, and monochromatic opacities, among other properties.

Avocado produces classifications of arbitrary astronomical transients and variable objects. It addresses the problem of biased spectroscopic samples by generating many lightcurves from each object in the original spectroscopic sample at a variety of redshifts and with many different observing conditions. The "augmented" samples of lightcurves that are generated are much more representative of the full datasets than the original spectroscopic samples.

aXe is a spectroscopic data extraction software package that was designed to handle large format spectroscopic slitless images such as those from the Wide Field Camera 3 (WFC3) and the Advanced Camera for Surveys (ACS) on HST. aXe is a PyRAF/IRAF package that consists of several tasks and is distributed as part of the Space Telescope Data Analysis System (STSDAS). The various aXe tasks perform specific parts of the extraction and calibration process and are successively used to produce extracted spectra.

axionCAMB is a modified version of the publicly available code CAMB (ascl:1102.026). axionCAMB computes cosmological observables for comparison with data. This is normally the CMB power spectra (T,E,B,\phi in auto and cross power), but also includes the matter power spectrum.

axionHMcode computes the non-linear matter power spectrum in a mixed dark matter cosmology with ultra-light axion (ULA) component of the dark matter. This model uses some of the fitting parameters and is inspired by HMcode (ascl:1508.001). axionHMcode uses the full expanded power spectrum to calculate the non-linear power spectrum; it splits the axion overdensity into a clustered and linear component to take the non clustering of axions on small scales due to free-streaming into account.

AxionNS computes radio light curves resulting from the resonant conversion of Axion dark matter into photons within the magnetosphere of a neutron star. Photon trajectories are traced from the observer to the magnetosphere where a root finding algorithm identifies the regions of resonant conversion. Given the modeling of the axion dark matter distribution and conversion probability, one can compute the photon flux emitted from these regions. The individual contributions from all the trajectories is then summed to obtain the radiated photon power per unit solid angle.

aztekas solves hyperbolic partial differential equations in conservative form using High Resolution Shock-Capturing (HRSC) schemes. The code can solve the non-relativistic and relativistic hydrodynamic equations of motion (Euler equations) for a perfect fluid. The relativistic part can solve these equations on a background fixed metric, such as for Schwarzschild, Minkowski, Kerr-Schild, and others.

BACCHUS (Brussels Automatic Code for Characterizing High accUracy Spectra) derives stellar parameters (T_eff, log g, metallicity, microturbulence velocity and rotational velocity), equivalent widths, and abundances. The code includes on the fly spectrum synthesis, local continuum normalization, estimation of local S/N, automatic line masking, four methods for abundance determinations, and a flagging system aiding line selection. BACCHUS relies on the grid of MARCS model atmospheres, Masseron's model atmosphere thermodynamic structure interpolator, and the radiative transfer code Turbospectrum (ascl:1205.004).

baccoemu provides a collection of emulators for large-scale structure statistics over a wide range of cosmologies. The emulators provide fast predictions for the linear cold- and total-matter power spectrum, the nonlinear cold-matter power spectrum, and the modifications to the cold-matter power spectrum caused by baryonic physics in a wide cosmological parameter space, including dynamical dark energy and massive neutrinos.

Directly imaged planet candidates (high contrast point sources near bright stars) are often validated, among other supporting lines of evidence, by comparing their observed motion against the projected motion of a background source due to the proper motion of the bright star and the parallax motion due to the Earth's orbit. Often, the "background track" is constructed assuming an interloping point source is at infinity and has no proper motion itself, but this assumption can fail, producing false positive results, for crowded fields or insufficient observing time-baselines (e.g. Nielsen et al. 2017). `backtrack` is a tool for constructing background proper motion and parallax tracks for validation of high contrast candidates. It can produce classical infinite distance, stationary background tracks, but was constructed in order to fit finite distance, non-stationary tracks using nested sampling (and can be used on clusters). The code sets priors on parallax based on the relations in Bailer-Jones et al. 2021 that are fit to Gaia eDR3 data, and are therefore representative of the galactic stellar density. The public example currently reproduces the results of Nielsen et al. 2017 and Wagner et al. 2022, demonstrating that the motion of HD 131399A "b" is fit by a finite distance, non-stationary background star, but the code has been tested and validated on proprietary datasets. The code is open source, available on github, and additional contributions are welcome.

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.

BAGEMASS calculates the posterior probability distribution for the mass and age of a star from its observed mean density and other observable quantities using a grid of stellar models that densely samples the relevant parameter space. It is written in Fortran and requires FITSIO (ascl:1010.001).

Bagpipes generates realistic model galaxy spectra and fits these to spectroscopic and photometric observations.

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

The Balrog package of Python simulation code is for use with real astronomical imaging data. Objects are simulated into a survey's images and measurement software is run over the simulated objects' images. Balrog allows the user to derive the mapping between what is actually measured and the input truth. The package uses GalSim (ascl:1402.009) for all object simulations; source extraction and measurement is performed by SExtractor (ascl:1010.064). Balrog facilitates the ease of running these codes en masse over many images, automating useful GalSim and SExtractor functionality, as well as filling in many bookkeeping steps along the way.

BALRoGO (Bayesian Astrometric Likelihood Recovery of Galactic Objects) handles data from the Gaia space mission. It extracts galactic objects such as globular clusters and dwarf galaxies from data contaminated by interlopers using a combination of Bayesian and non-Bayesian approaches. It fits proper motion space, surface density, and the object center. It also provides confidence regions for the color-magnitude diagram and parallaxes.

BAMBI (Blind Accelerated Multimodal Bayesian Inference) is a Bayesian inference engine that combines the benefits of SkyNet (ascl:1312.007) with MultiNest (ascl:1109.006). It operated by simultaneously performing Bayesian inference using MultiNest and learning the likelihood function using SkyNet. Once SkyNet has learnt the likelihood to sufficient accuracy, inference finishes almost instantaneously.

bamr is an MPI implementation of a Bayesian analysis of neutron star mass and radius data that determines the mass versus radius curve and the equation of state of dense matter. Written in C++, bamr provides some EOS models. This code requires O₂scl (ascl:1408.019) be installed before compilation.

Bandmerge takes in ASCII tables of positions and fluxes of detected astronomical sources in 2-7 different wavebands, and write out a single table of the merged data. The tool was designed to work with source lists generated by the Spitzer Science Center's MOPEX (ascl:1111.006) software, although it can be "fooled" into running on other data as well.

BANG (BAyesian decomposiotioN of Galaxies) models both the photometry and kinematics of galaxies. The underlying model is the superposition of different components with three possible combinations: 1.) Bulge + inner disc + outer disc + Halo; 2.) Bulge + disc + Halo; and 3.) inner disc + outer disc + Halo. As CPU parameter estimation can take days, running BANG on GPU is recommended.

BANYAN_Sigma calculates the membership probability that a given astrophysical object belongs to one of the currently known 27 young associations within 150 pc of the Sun, using Bayesian inference. This tool uses the sky position and proper motion measurements of an object, with optional radial velocity (RV) and distance (D) measurements, to derive a Bayesian membership probability. By default, the priors are adjusted such that a probability threshold of 90% will recover 50%, 68%, 82% or 90% of true association members depending on what observables are input (only sky position and proper motion, with RV, with D, with both RV and D, respectively). The algorithm is implemented in a Python package, in IDL, and is also implemented as an interactive web page.

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

BANZAI (Beautiful Algorithms to Normalize Zillions of Astronomical Images) processes raw data taken from Las Cumbres Observatory and produces science quality data products. It is capable of reducing single or multi-extension fits files. For historical data, BANZAI can also reduce the data cubes that were produced by the Sinistro cameras.

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

baofit analyzes cosmological correlation functions to estimate parameters related to baryon acoustic oscillations and redshift-space distortions. It has primarily been used to analyze Lyman-alpha forest autocorrelations and cross correlations with the quasar number density in BOSS data. Fit models are fully three-dimensional and include flexible treatments of redshift-space distortions, anisotropic non-linear broadening, and broadband distortions.

Using the 2-point correlation function, BAOlab aids the study of Baryon Acoustic Oscillations (BAO). The code generates a model-dependent covariance matrix which can change the results both for BAO detection and for parameter constraints.

BAOlab is an image processing package written in C that should run on nearly any UNIX system with just the standard C libraries. It reads and writes images in standard FITS format; 16- and 32-bit integer as well as 32-bit floating-point formats are supported. Multi-extension FITS files are currently not supported. Among its tools are ishape for size measurements of compact sources, mksynth for generating synthetic images consisting of a background signal including Poisson noise and a number of pointlike sources, imconvol for convolving two images (a “source” and a “kernel”) with each other using fast fourier transforms (FFTs) and storing the output as a new image, and kfit2d for fitting a two-dimensional King model to an image.

Barcode (BAyesian Reconstruction of COsmic DEnsity fields) samples the primordial density fields compatible with a set of dark matter density tracers after cosmic evolution observed in redshift space. It uses a redshift space model based on the analytic solution of coherent flows within a Hamiltonian Monte Carlo posterior sampling of the primordial density field; this method is applicable to analytically derivable structure formation models, such as the Zel'dovich approximation, but also higher order schemes such as augmented Lagrangian perturbation theory or even particle mesh models. The algorithm is well-suited for analysis of the dark matter cosmic web implied by the observed spatial distribution of galaxy clusters, such as obtained from X-ray, SZ or weak lensing surveys, as well as that of the intergalactic medium sampled by the Lyman alpha forest. In these cases, virialized motions are negligible and the tracers cannot be modeled as point-like objects. Barcode can be used in all of these contexts as a baryon acoustic oscillation reconstruction algorithm.

Barry compares different BAO models. It removes as many barriers and complications to BAO model fitting as possible and allows each component of the process to remain independent, allowing for detailed comparisons of individual parts. It contains datasets, model fitting tools, and model implementations incorporating different descriptions of non-linear physics and algorithms for isolating the BAO (Baryon Acoustic Oscillation) feature.

BART implements a Bayesian, Monte Carlo-driven, radiative-transfer scheme for extracting parameters from spectra of planetary atmospheres. BART combines a thermochemical-equilibrium code, a one-dimensional line-by-line radiative-transfer code, and the Multi-core Markov-chain Monte Carlo statistical module to constrain the atmospheric temperature and chemical-abundance profiles of exoplanets.

BARYCORR is a Python interface for ZBARYCORR (ascl:1807.017); it requires the measured redshift and returns the corrected barycentric velocity and time correction.

barycorrpy (BCPy) is a Python implementation of Wright and Eastman's 2014 code (ascl:1807.017) that calculates precise barycentric corrections well below the 1 cm/s level. This level of precision is required in the search for 1 Earth mass planets in the Habitable Zones of Sun-like stars by the Radial Velocity (RV) method, where the maximum semi-amplitude is about 9 cm/s. BCPy was developed for the pipeline for the next generation Doppler Spectrometers - Habitable-zone Planet Finder (HPF) and NEID. An automated leap second management routine improves upon the one available in Astropy. It checks for and downloads a new leap second file before converting from the UT time scale to TDB. The code also includes a converter for JDUTC to BJDTDB.

baryon-sweep produces a robust outlier rejection while simultaneously preserving the signal of the science target. The code works as a standalone solution or as a supplement to the current pipeline software. baryon-sweep creates the 2D pixel mask and mask layers, processes the sky (non-science target) spaxels, and creates a post-processed cube ready for use.

BASCS models spatial and spectral information from overlapping sources and the background, and jointly estimates all individual source parameters. The use of spectral information improves the detection of both faint and closely overlapping sources and increases the accuracy with which source parameters are inferred.

The BASE-9 (Bayesian Analysis for Stellar Evolution with nine variables) software suite recovers star cluster and stellar parameters from photometry and is useful for analyzing single-age, single-metallicity star clusters, binaries, or single stars, and for simulating such systems. BASE-9 uses a Markov chain Monte Carlo (MCMC) technique along with brute force numerical integration to estimate the posterior probability distribution for the age, metallicity, helium abundance, distance modulus, line-of-sight absorption, and parameters of the initial-final mass relation (IFMR) for a cluster, and for the primary mass, secondary mass (if a binary), and cluster probability for every potential cluster member. The MCMC technique is used for the cluster quantities (the first six items listed above) and numerical integration is used for the stellar quantities (the last three items in the above list).

BASE is a novel program for the combined or separate Bayesian analysis of astrometric and radial-velocity measurements of potential exoplanet hosts and binary stars. The tool fulfills two major tasks of exoplanet science, namely the detection of exoplanets and the characterization of their orbits. BASE was developed to provide the possibility of an integrated Bayesian analysis of stellar astrometric and Doppler-spectroscopic measurements with respect to their binary or planetary companions’ signals, correctly treating the astrometric measurement uncertainties and allowing to explore the whole parameter space without the need for informative prior constraints. The tool automatically diagnoses convergence of its Markov chain Monte Carlo (MCMC[2]) sampler to the posterior and regularly outputs status information. For orbit characterization, BASE delivers important results such as the probability densities and correlations of model parameters and derived quantities. BASE is a highly configurable command-line tool developed in Fortran 2008 and compiled with GFortran. Options can be used to control the program’s behaviour and supply information such as the stellar mass or prior information. Any option can be supplied in a configuration file and/or on the command line.

BASIN (Beowulf Analysis Symbolic INterface) is a flexible, integrated suite of tools for multiuser parallel data analysis and visualization that allows researchers to harness the power of Beowulf PC clusters and multi-processor machines without necessarily being experts in parallel programming. It also includes general tools for data distribution and parallel operations on distributed data for developing libraries for specific tasks.

BASTA determines properties of stars using a pre-computed grid of stellar models. It calculates the probability density function of a given stellar property based on a set of observational constraints defined by the user. BASTA is very versatile and has been used in a large variety of studies requiring robust determination of fundamental stellar properties.

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

batman provides fast calculation of exoplanet transit light curves and supports calculation of light curves for any radially symmetric stellar limb darkening law. It uses an integration algorithm for models that cannot be quickly calculated analytically, and in typical use, the batman Python package can calculate a million model light curves in well under ten minutes for any limb darkening profile.

Bayesian Technique for Multi-image Analysis (BaTMAn) characterizes any astronomical dataset containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (i.e. identical signal within the errors). The output segmentations successfully adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. BaTMAn identifies (and keeps) all the statistically-significant information contained in the input multi-image (e.g. an IFS datacube). The main aim of the algorithm is to characterize spatially-resolved data prior to their analysis.

BAYES-LOSVD performs non-parametric extraction of the Line-Of-Sight Velocity Distributions in galaxies. Written in Python, it uses Stan (ascl:1801.003) to perform all the computations and provides reliable uncertainties for all the parameters of the model chosen for the fit. The code comes with a large number of features, including read-in routines for some of the most popular IFU spectrographs and surveys, such as ATLAS3D, CALIFA, MaNGA, MUSE-WFM, SAMI, and SAURON.

The great majority of X-ray measurements of cluster masses in the literature assume parametrized functional forms for the radial distribution of two independent cluster thermodynamic properties, such as electron density and temperature, to model the X-ray surface brightness. These radial profiles (e.g. β-model) have an amplitude normalization parameter and two or more shape parameters. BAYES-X uses a cluster model to parametrize the radial X-ray surface brightness profile and explore the constraints on both model parameters and physical parameters. Bayes-X is programmed in Fortran and uses MultiNest (ascl:1109.006) as the Bayesian inference engine.