Procoli extracts profile likelihoods in cosmology. It wraps MontePython (ascl:1805.027), the fast sampler written specifically for CLASS (ascl:1106.020). All likelihoods available for use with MontePython are hence immediately available for use. Procoli is based on a simulated-annealing optimizer to find the global maximum likelihoods value as well as the maximum likelihood points along the profile of any use input parameter.

CAMEL (Cosmological Analysis with Minuit Exploration of the Likelihood) performs cosmological parameters estimations using best fits, Monte-Carlo Markov Chains, and profile-likelihoods. Widely used in Planck satellite data analysis, by default it employs CLASS (ascl:1106.020) to compute all relevant cosmological quantities, but any other Boltzmann solver can easily be plugged in.

pinc ("profiles in cosmology") computes profile likelihoods in cosmology; it can also determine the (boundary-corrected) confidence intervals with the graphical construction. The code uses a simulated annealing scheme and interfaces with MontePython (ascl:1805.027). pinc consists of three short scripts; these automatically set the relevant parameters in MontePython, submit the minimization chains, and analyze the results.

OK Binaries is a tool for identifying suitable calibration binaries from the Washington Double Star (WDS) Sixth Orbit Catalog. It calculates orbital positions at any epoch, propagates uncertainties using Monte Carlo sampling, and generates orbit plots. The web app includes automated daily updates of binary positions and a searchable interface with filters for position, magnitude, separation, and other orbital parameters. OK Binaries can be used online, as a standalone offline browser app, or via the command line.

CLUES (CLustering UnsupErvised with Sequencer) analyzes spectral and IFU data. This fully interpretable clustering tool uses machine learning to classify and reduce the effective dimensionality of data sets. It combines multiple unsupervised clustering methods with multiscale distance measures using Sequencer (ascl:2105.006) to find representative end-member spectra that can be analyzed with detailed mineralogical modeling and follow-up observations. CLUES has been used on Spitzer IRS data and debris disk science, and can be applied to other high-dimensional spectral data sets, including mineral spectroscopy in general areas of astrophysics and remote sensing.

Bjet_MCMC automatically models multiwavelength spectral energy distributions of blazars, considering one-zone synchrotron-self-Compton (SSC) model with or without the addition of external inverse-Compton process from the thermal emission of the nucleus. The code also contains manual fitting functionalities for multi-zone SSC modeling. Bjet_MCMC is built as an MCMC python wrapper around the C++ code Bjet.

pynchrotron implements synchrotron emission from cooling electrons. It removes the need for GSL which was originally relied on for a quick computation of the synchrotron kernel. The code has been ported from GSL and written directly in python as well as accelerated with numba. pynchrotron also includes an astromodels (ascl:2506.019) function for direct use in 3ML (ascl:2506.018).

Astromodels defines models for likelihood or Bayesian analysis of astrophysical data. Though designed for analysis in the spectral domain, it can also be used as a toolbox containing functions of any variable. Astromodels is not a modeling package; it provides the tools to build a model as complex as one needs. A separate package such as 3ML (ascl:2506.018) is needed to fit the model to the data.

The Multi-Mission Maximum Likelihood framework (3ML) provides a common high-level interface and model definition for coherent and intuitive modeling of sources using all the available data, no matter their origin. Astrophysical sources are observed by different instruments at different wavelengths with an unprecedented quality, and each instrument and data type has its own ad-hoc software and handling procedure. 3ML's architecture is based on plug-ins; the package uses the official software of each instrument under the hood, thus guaranteeing that 3ML is always using the best possible methodology to deal with the data of each instrument. Though Maximum Likelihood is in the name for historical reasons, 3ML is an interface to several Bayesian inference algorithms such as MCMC and nested sampling as well as likelihood optimization algorithms.

Hydromass analyzes galaxy cluster mass profiles from X-ray and/or Sunyaev-Zel’dovich observations. It provides a global Bayesian framework for deprojection and mass profile reconstruction, including mass model fitting, forward fitting with parametric and polytropic models, and non-parametric log-normal mixture reconstruction. Hydromass easily loads public X-COP data products and applies reconstruction tools directly within a Jupyter notebook.

SBI++ is a complete methodology based on simulation-based (likelihood-free) inference that is customized for astronomical applications. Specifically, the code retains the fast inference speed of ∼1 sec for objects in the observational training set distribution, and additionally permits parameter inference outside of the trained noise and data at ~1 min per object. The package includes scripts for training and implementing SBI++ and is dependent on sbi (ascl:2306.002).

Octofitter performs Bayesian inference against a wide variety of exoplanet and binary star data. It is highly modular and allows users to easily adjust priors, change parameterizations, and specify arbitrary function relations between the parameters of one or more planets. Octofitter further supplies tools for examining model outputs including prior and posterior predictive checks and simulation based calibration.

M_-M_K- converts absolute 2MASS Ks-band magnitude (or a distance and a Ks-band magnitude) into an estimate of the stellar mass using the empirical relation derived from the resolved photometry and orbits of astrometric binaries. The code requires scalar values for K, distance, and corresponding uncertainties. M_-M_K- outputs errors based on the relationship's scatter and errors in the provided distance and Ks magnitude.

This One-Class Support Vector Machine (SVM) model detects exoplanet transit events. One-class SVMs fit data and make predictions faster than simple CNNs, and do not require specialized equipment such as Graphics Processing Units (GPU). The code uses a Gaussian kernel to compute a nonlinear decision boundary. After training, OCSVM-Transit-Detection requires that lightcurves classified as containing a transit have features very similar to the lightcurves in the training dataset, thus limiting misclassifications.

The Python wrapper pyTPCI couples newer versions of the hydrodynamics code PLUTO (ascl:1010.045) and the gas microphysics code CLOUDY (ascl:9910.001) to self-consistently simulate escaping atmospheres in 1D. Following TPCI (ascl:2506.012), on which pyTPCI is based, CLOUDY is modified to read in depth-dependent wind velocities, and to output useful physical quantities (including mass density, number density, and mean molecular weight as a function of depth).

The PLUTO CLOUDY Interface (TPCI) combines the PLUTO (ascl:1010.045) and CLOUDY (ascl.net:9910.001) simulation codes to simulate hydrodynamic evolution under irradiation from a source. The code solves the photoionization and chemical network of the 30 lightest elements. By combining an equilibrium photoionization solver with a general MHD code, TPCI provides an advanced simulation tool applicable to a variety of astrophysical problems.

easyCHEM calculates chemical equilibrium abundances (including condensation) and adiabatic gradients by minimization of the so-called Gibbs free energy. Ancillary outputs are the atmospheric adiabatic temperature gradient and mean molar mass. Because easyCHEM incorporates the dgesv routine from LAPACK (ascl:2104.020) for fast matrix inversion,external math libraries are not required.

GRIP (Generic data Reduction for nulling Interferometry Package) reduces nulling data with enhanced statistical self-calibration methods from any nulling interferometric instrument within a single and consistent framework. The toolbox self-calibrates null depth measurements by fitting a model of the instrumental perturbations to histograms of data. The model is generated using a simulator of the instrument built into the package for the main operating nullers or provided by the user. GRIP handles baseline discrimination and spectral dispersion and features several optimizing strategy, including least squares, maximum likelihood, and MCMC with emcee (ascl:1303.002), and works on GPU using the cupy library.

DART-Vetter distinguishes planetary candidates from false positives detected in any transiting survey, and is tailored for photometric data collected from space-based missions. The Convolutional Neural Network is trained on Kepler and TESS Threshold Crossing Events (TCEs), and processes only light curves folded on the period of the relative signal. DART-Vetter has a simple and compact architecture; it is lightweight enough to be executed on personal laptops.

The excalibuhr end-to-end pipeline extracts high-resolution spectra designed for VLT/CRIRES+. The package preprocesses raw calibration files, including darks, flats, and lamp frames, and can trace spectral orders on 2D detector images. It applies calibrations to science frames, can remove the sky background by nodding subtraction, and combines frames per nodding position. excalibuhr can also extract 1D spectrum and perform wavelength and flux calibration.

Gen TSO estimates signal-to-noise ratios for transit/eclipse depths through an interactive graphical interface, similar to the JWST Exposure Time Calculator (ETC). This interface leverages the ETC by combining its noise simulator, Pandeia, with additional exoplanet resources from the NASA Exoplanet Archive, the Gaia DR3 catalog, and the TrExoLiSTS database of JWST programs. Gen TSO calculates S/Ns for all JWST instruments for the spectroscopic time-series modes available as of the Cycle 4 GO call. It also simulates target acquisition on the science targets or, when needed, on nearby stellar targets.

VBMicrolensing performs efficient computation in gravitational microlensing events using the advanced contour integration method, supporting single, binary and multiple lenses. It calculates magnification by single, binary and multiple lenses, centroid of the images generated by single and binary lenses, and critical curves and caustics of binary and multiple lenses. It also computes complete light curves including several higher order effects, such as limb darkening of the source, binary source, parallax, xallarap, and circular and elliptic orbital motion.

VBMicrolensing is written as a C++ library and wrapped as a Python package; the code can be called from either C++ or Python. This package encompasses VBBinaryLensing (ascl:1809.004), which is at the basis of several platforms for microlensing modeling. VBBinaryLensing will still be available as a legacy software, but will no longer be maintained.

TESS-cont quantifies the flux fraction coming from nearby stars in the TESS photometric aperture of any observed target. The package identifies the main contaminant Gaia DR2/DR3 sources, quantifies their individual and total flux contributions to the aperture, and determines whether any of these stars could be the origin of the observed transit and variability signals. Written in Python, TESS-cont is based on building the pixel response functions (PRFs) of nearby Gaia sources and computing their flux distributions across the TESS Target Pixel Files (TPFs) or Full Frame Images (FFIs).

SMART (Spectral Modeling Analysis and RV Tool) forward models spectral data. The method works best in those spectral orders with both strong telluric absorption features for accurate wavelength calibration and sufficient structure in the stellar spectrum to distinguish it from the telluric absorption. The code uses Markov Chain Monte Carlo (MCMC) methods to determine stellar parameters such as effective temperature, surface gravity, and rotational velocity, and calibration factors, including continuum and wavelength corrections, instrumental line-spread function (LSF), and strength of telluric absorption. SMART has been used with Keck/NIRSPEC, SDSS/APOGEE, Gemini/IGRINS high-resolution near-infrared spectrometers, among others, and with medium-resolution spectrometers, including Keck/OSIRIS and Keck/NIRES

The MAGIC (Microlensing Analysis Guided by Intelligent Computation) PyTorch framework efficiently and accurately infers the microlensing parameters of binary events with realistic data quality. The code divides binary microlensing parameters into two groups, which are inferred separately with different neural networks. The neural controlled differential equation handles light curves with irregular sampling and large data gaps. MAGIC can achieve fractional uncertainties of a few percent on the binary mass ratio and separation, and can locate the degenerate solutions even when large data gaps are introduced. As irregular samplings are common in astronomical surveys, this code may be useful for other time series studies.

Cumulative Time Dilation (CTD) calculates and plots the total time dilation experienced by a point (Earth) located at the center of a spherical mass-energy distribution. There are both analytical and numerical solutions for two different descriptions of how gravity acts across cosmological distances. The calculations are done for universes filled with a single energy type (dark energy; matter, including dark matter; or radiation) as well as the concordance model.

Hibridon solves the close-coupled equations which occur in the quantum treatment of inelastic atomic and molecular collisions. Gas-phase scattering, photodissociation, collisions of atoms and/or molecules with flat surfaces, and bound states of weakly-bound complexes can be treated.

The AIRI (AI for Regularization in radio-interferometric Imaging) algorithms are Plug-and-Play (PnP) algorithms propelled by learned regularization denoisers and endowed with robust convergence guarantees. The (unconstrained) AIRI algorithm is built on a Forward-Backward optimization algorithmic backbone enabling handling soft data-fidelity terms. AIRI's primary application is to solve large-scale high-resolution high-dynamic range inverse problems for RI in radio astronomy, more specifically 2D planar monochromatic intensity imaging.

The SCATTERING code solves the coupled equations for a given scattering system, provides the scattering S-matrix elements, and calculates the state-to-state cross-sections. Its approach is different from codes such as MOLSCAT (ascl:1206.004) or Hibridon (ascl:2505.020), as SCATTERING solves coupled equations in the body-fixed (BF) frame, where the coupling matrix exhibits a predominantly block-diagonal structure with blocks interconnected by centrifugal terms. This significantly reduces computational time and memory requirements.

TD-CARMA estimates cosmological time delays to model observed and irregularly sampled light curves as realizations of a continuous auto-regressive moving average (CARMA) process using MultiNest (ascl:1109.006) for Bayesian inference. TD-CARMA accounts for heteroskedastic measurement errors and microlensing, an additional source of independent extrinsic long-term variability in the source brightness.

iSLAT (the interactive Spectral-Line Analysis Tool) provides an interactive interface for the visualization, exploration, and analysis of molecular spectra. Synthetic spectra are made using a simple slab model; the code uses molecular data from HITRAN. iSLAT has been tested on spectra at infrared wavelengths as observed at different resolving powers (R = 700-90,000) with JWST-MIRI, Spitzer-IRS, VLT-CRIRES, and IRTF-ISHELL.

CETRA (Cambridge Exoplanet Transit Recovery Algorithm) detects transit by performing a linear transit search followed by a phase-folding of the former into a periodic signal search, using a physically motivated transit model to improve detection sensitivity. Implemented with NVIDIA’s CUDA platform, the code outperforms traditional methods like Box Least Squares and Transit Least Squares in both sensitivity and speed. It can also be used to identify transits that aren't periodic in the input light curve. CETRA is designed to be run on detrended light curves.

afterglowpy models Gamma-ray burst afterglows. It computes synchrotron radiation from an external shock and is capable of handling both structured jets and off-axis observers. The code provides fully trans-relativistic shock evolution through a constant density medium, on-the-fly integration over the equal-observer-time slices of the shock surface, and includes an approximate prescription for jet spreading. afterglowpy has been calibrated to the BoxFit code (ascl:2306.059) and produces similar light curves for top hat jets (within 50% when same parameters are used) both on- and off-axis.

tBilby is a trans-dimensional Bayesian inference tool based on the Bilby (ascl:1901.011) inference package. It provides tools and examples to facilitate trans-dimensional Bayesian inference and offers a high degree of flexibility in constructing models and defining priors. tBilby seeks to further develop trans-dimensional Bayesian inference.

The 1D cloud model code ExoLyn solves the transport equation of cloud particles and vapor under cloud condensation rates that are self-consistently calculated from thermodynamics. It can be combined with optool (ascl:2104.010) to calculate solid opacities and with petitRADTRANS (ascl:2207.014) to generate transmission or emission spectra. The code balances physical consistency with computational efficiency, opening the possibility of joint retrieval of exoplanets' gas and cloud components. ExoLyn has been designed to study cloud formation across a variety of planets, such as hot Jupiters, sub-Neptunes, and self-luminous planets.

Eclipsoid provides a general framework allowing rotational deformation to be modeled in transits, occultations, phase curves, transmission spectra and more of bodies in orbit around each other, such as an exoplanet orbiting a host star. It is an extension of jaxoplanet (ascl:2504.028).

Exo-MerCat generates a catalog of known and candidate exoplanets, collecting and selecting the most precise measurement for all interesting planetary and orbital parameters contained in exoplanet databases. It retrieves a common name for the planet target, linking its host star name with the preferred identifier in the most well-known stellar databases, and accounts for the presence of multiple aliases for the same target. The code standardizes the output and notation differences and homogenizes the data in a VO-aware way. Exo-MerCat also provides a graphical user interface to filter data based on the user's constraints and generate automatic plots that are commonly used in the exoplanetary community.

BEM predicts the radius of exoplanets based on their planetary and stellar parameters. The code uses the random forests machine learning algorithm to derive reliable radii, especially for planets between 4 R⊕ and 20 R⊕ for which the error is under 25%. BEM computes error bars for the radius predictions and can also create diagnostic plots.

The Aeolus library, written in Python, analyzes and plots climate model output using modules to work with 3D general circulation models of planetary atmospheres. The code provides various functions tailored to exoplanet research, e.g., in the context of tidally-locked exoplanets. Generic (planet-independent constants) and basic constants of the Earth atmosphere are also provided. Aeolus can store model-specific variable and coordinate names in one container, which can be passed to various functions, and can also calculate the synthetic transmission spectrum.

Jitter predicts radial-velocity (RV) jitter due to stellar oscillations and granulation, in terms of various sets of fundamental stellar properties. The code can also be used to set a prior for the jitter term as a component when modeling the Keplerian orbits of the exoplanets.

jnkepler models photometric and radial velocity data of multi-planet systems via N-body integration. Built with JAX, it leverages automatic differentiation for efficient computation of model gradients. This enables seamless integration with gradient-based optimizers and Hamiltonian Monte Carlo methods, including the No-U-Turn Sampler (NUTS) in NumPyro (ascl:2505.005). jnkepler is particularly suited for efficiently sampling from multi-planet posteriors involving a larger number of parameters and strong degeneracy.

The lightweight probabilistic programming library NumPyro provides a NumPy backend for Pyro (ascl:2110.016). It relies on JAX for automatic differentiation and JIT compilation to GPU/CPU. The code focuses on providing a flexible substrate for users to build on, including Pyro Primitives, inference algorithms with a particular focus on MCMC algorithms such as Hamiltonian Monte Carlo, and distribution classes, constraints and bijective transforms. NumPyro also provides effect-handlers that can be extended to implement custom inference algorithms and inference utilities.

Eureka! reduces and analyzes exoplanet time-series observations; though particularly focused on JWST data, it also handles HST observations. Starting with raw, uncalibrated FITS files, it reduces time-series data to precise exoplanet transmission and emission spectra. The code can perform flat-fielding, unit conversion, background subtraction, and optimal spectral extraction. It can generate a time series of 1D spectra for spectroscopic observations and a single light curve of flux versus time for photometric observations. Eureka! can also fit light curves with noise and astrophysical models using different optimization or sampling algorithms and is able to display the planet spectrum in figure and table form.

pyGCG provides a graphical user interface for viewing and classifying NIRISS-WFSS data products. Though originally designed for use by the GLASS-JWST collaboration, this software has been tested against the data products from the PASSAGE collaboration as well. pyGCG allows users to interactively browse a selection of reduced data products with the option of also writing classifications to a table.

SWIFTGalaxy analyzes particles belonging to individual simulated galaxies. The code provides a software abstraction of simulated galaxies produced by the SWIFT smoothed particle hydrodynamics code (ascl:1805.020) and extends the SWIFTSimIO module. SWIFTGalaxy inherits from and extends the functionality of the SWIFTDataset. It understands the output of halo finders and therefore which particles belong to a galaxy and its integrated properties. The particles occupy a coordinate frame that is enforced to be consistent, such that particles loaded on-the-fly will, for example, match rotations and translations of particles already in memory. Intuitive masking of particle datasets is also enabled. Finally, SWIFTGalaxy provides utilities that make working in cylindrical and spherical coordinate systems more convenient.

speclib provides a lightweight Python interface for loading, manipulating, and analyzing stellar spectra and model grids. The code can load a spectral grid into memory and linearly interpolate between temperature grid points to generate component spectra. speclib includes utilities for photometric synthesis, spectral resampling, and SED construction using stellar spectral libraries.

This modular Python-based pipeline provides tools for computing background cosmological quantities and Fourier-space power spectra for multiple tracers of large-scale structure, such as galaxies and 21cm intensity maps. It is designed for multitracer Fisher forecasting in both the linear regime and nonlinear scales using HALOFIT. The pipeline enables forecasts of cosmological parameters such as f_NL, fσ8, and tracer bias parameters. Its flexible architecture includes independently callable modules for the Hubble parameter, comoving distance, growth functions, matter power spectrum, transfer functions, and cross-power spectrum combinations. The code supports both theoretical survey design and nonlinear parameter estimation, making it suitable for a wide range of cosmological analyses.

Sapphire++ is an open-source code designed to numerically solve the Vlasov–Fokker–Planck equation for astrophysical applications. Sapphire++ employs a numerical algorithm based on a spherical harmonic expansion of the distribution function, expressing the Vlasov–Fokker–Planck equation as a system of partial differential equations governing the evolution of the expansion coefficients. The code utilises the discontinuous Galerkin method in conjunction with implicit and explicit time stepping methods to compute these coefficients, providing significant flexibility in its choice of spatial and temporal accuracy.

infrared_comparison compares the downwelling infrared radiation, or sky spectral brightness, of arctic/antarctic astronomical observing sites with the best mid-latitude mountain sites. The code site provides a tarfile of Fourier-transform spectra from 3.3 microns 20 microns, obtained near Eureka, on Ellesmere Island Canada, along with meteorological data. The code can compare these via an atmospheric thermal-inversion model to reported values for South Pole and other mid-latitude sites, such as Maunakea.

arctic_mass_dimm reduces data from the Multi-Aperture Seeing Sensor (MASS) and Differential Image Motion Monitor (MASS) obtained from the Polar Environment Atmospheric Research Laboratory (PEARL), reporting seeing conditions, and comparing to other observatories. The code site provides a tarfile of all MASS and DIMM data obtained near Eureka, on Ellesmere Island Canada in 2011/12 along with associated meteorological data. The code employs a simple two-component atmospheric model to allow comparison of PEARL to mid-latitude sites such as Maunakea.