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 (https://ascl.net/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.

allsky performs photometry of Polaris with the Polar Environment Atmospheric Research Laboratory (PEARL) All-Sky Camera (PASI) to report transparency measurements, with comparison to conditions at other observatories worldwide. The code site provides a tarfile of PASI data obtained near Eureka, on Ellesmere Island Canada in darktime of 2008/09 and 2009/10 along with associated meteorological data. The code employs a simple atmospheric thermal inversion model, with a power-law fit to ice-crystal attenuation, allowing direct comparison of PEARL dark-time photometric-sky statistics to mid-latitude sites such as Maunakea.

arctic_weather reports analysis of meteorological data recorded from High Arctic weatherstations (called Inuksuit) deployed on coastal mountains north of 80 degrees on Ellesmere Island Canada from 2006 through 2009, along with clear-sky fractions from horizon-viewing sky-monitoring cameras. The code calculates solar and lunar elevations, and so allows correlation of polar nighttime to the development of prevailing thermal inversion conditions in winter, and statistical comparison to other optical/infrared observatory sites.

We introduce COBRA (Cosmology with Optimally factorized Bases for Rapid Approximation), a novel framework for rapid computation of large-scale structure observables. COBRA separates scale dependence from cosmological parameters in the linear matter power spectrum while also minimising the number of necessary basis terms, thus enabling direct and efficient computation of derived and nonlinear observables. Moreover, the dependence on cosmological parameters is efficiently approximated using radial basis function interpolation. We apply our framework to decompose the linear matter power spectrum in the standard LCDM scenario, as well as by adding curvature, dynamical dark energy and massive neutrinos, covering all redshifts relevant for Stage IV surveys. With only a dozen basis terms, COBRA reproduces exact Boltzmann solver calculations to 0.1% precision, which improves further to 0.02% in the pure LCDM scenario. Using our decomposition, we recast the one-loop redshift space galaxy power spectrum in a separable minimal-basis form, enabling $\sim 4000$ model evaluations per second at 0.02% precision on a single thread. This constitutes a considerable improvement over previously existing methods (e.g., FFTLog) opening a new window for efficient computations of higher loop and higher order correlators involving multiple powers of the linear matter power spectra. The resulting factorisation can also be utilised in clustering, weak lensing and CMB analyses. Our implementation is publicly available at https://github.com/ThomasBakx/cobra.

astromorph performs an automatic classification of astronomical objects based on their morphology using machine learning in a self-supervised manner. Written in Python, the pipeline is an implementation for astronomical images in FITS-format files of the Boot-strap Your Own Latents (BYOL; Grill et al. 2020) method, which does not require labelling of the training data.

show_cube displays the results of reducing, aligning and combining near-infrared integral field spectroscopy with the Gemini Observatory NIFS (Near-infrared Integral Field Spectrometer) instrument. Image slices are extracted from the raw data frames to make the input datacube. The code site also provides a tarfile containing all the raw NIFS FITS-format files for the observations of high-redshift radio galaxies 3C230, 3C294, and 4C+41.17, the last of which are reported, together with line-strengths using the MAPPINGS III (ascl:1306.008) shock models.

SHELLFISH (SHELL Finding In Spheroidal Halos) finds the splashback shells of individual halos within cosmological simulations. It uses a command line toolchain to produce human-readable catalogs. It requires a configuration file that describes the layout of the particle snapshots and halo catalog and which halos to measure the splashback shell for; once that is provided, Shellfish takes care of the rest. It supports numerous particle catalog types, including gotetra, Gadget-2, and Bolshoi, all text column-based halo catalogs, and consistent-trees merger trees.

JOFILUREN analyzes and de-noises scientific data and is useful for studying and reducing the physical effects of particle noise in particle-mesh computer simulations. It uses wavelets, which can efficiently remove noise from cosmological, galaxy and plasma N-body simulations. Written in Fortran, the code is portable and can be included in grid-based N-body codes. JOFILUREN can also be applied for removing noise from standard data, such as signals and images.

Vela.jl performs Bayesian pulsar timing and noise analysis. It supports narrowband and wideband TOAs along with most commonly used pulsar timing models. The code provides an independent, efficient, and parallelized implementation of the full nonlinear pulsar timing and noise model and includes a Python binding (pyvela). One-time operations such as data file input, clock corrections, and solar system ephemeris computations are performed by pyvela with the help of the PINT (ascl:1902.007) pulsar timing package.

DMCalc estimates the Dispersion Measure (DM) of wide-band pulsar data in psrfits format. It uses PSRCHIVE (ascl:1105.014) tools to get ToAs and then uses TEMPO2 (ascl:1210.015) for DM fitting. A median absolute deviation (MAD) based ToA rejection algorithm is implemented in the code to remove large outlier ToAs using Huber Regression. Although the code has been used for analyzing uGMRT wide-band data, DMCalc can in principle be used for any pulsar dataset.

TempoNest performs a Bayesian analysis of pulsar timing data, which allows for the robust determination of the non-linear pulsar timing solution simultaneously with a range of additional stochastic parameters. This includes both red spin noise and dispersion measure variations using either power law descriptions of the noise, or through a model-independent method that parameterizes the power at individual frequencies in the signal. It uses the Bayesian inference tool MultiNest (ascl:1109.006) to explore the joint parameter space, while using Tempo2 (ascl:1210.015) as a means of evaluating the timing model. TempoNest allows for the analysis of additional stochastic signals beyond the white noise described by the TOA error bars that may be present in the data.

The highly optimized Kotekan framework processes streaming data. Written in a combination of C/C++, it is primarily designed for use on radio telescopes and was originally developed for the CHIME project. It is similar to radio projects such as GNUradio (ascl:2504.029) or Bifrost (ascl:1711.021), though has a greater focus on efficiency and throughput. Kotekan is conceptually straightforward: data is carried through the system in a series of ring buffer objects, which are connected by processing blocks which manipulate the data, and optional metadata structures can be passed alongside the streaming data.

The GNU Radio toolkit provides signal processing blocks to implement software radios. A software radio performs signal processing in software instead of using dedicated integrated circuits in hardware. The benefit is that since software can be easily replaced in the radio system, the same hardware can be used to create many kinds of radios for many different communications standards. GNU Radio can be used with readily-available low-cost external RF hardware to create software-defined radios and to simulate wireless communications.

jaxoplanet is a functional-programming-forward implementation of many features from the exoplanet and starry packages built on top of JAX (ascl:2111.002). It includes fast and robust implementations of many exoplanet-specific operations, including solving Kepler’s equation, and computing limb-darkened light curves. jaxoplanet has first-class support for hardware acceleration using GPUs and TPUs, and integrates seamlessly with modeling tools such as NumPyro (ascl:2505.005) and Flax (ascl:2504.026).

picasso makes predictions for the thermodynamic properties of the gas in massive dark matter halos from gravity-only cosmological simulations. It combines an analytical model of gas properties as a function of gravitational potential with a neural network predicting the parameters of said model. Written in Python, it combines an implementation of the gas model based on JAX (ascl:2111.002) and Flax (ascl:2504.026), and models that have been pre-trained to reproduce gas properties from hydrodynamic simulations.

Flax provides a flexible end-to-end user experience for JAX users; its NNX is a simplified API that creates, inspects, debugs, and analyzes neural networks in JAX. It has first class support for Python reference semantics, enabling users to express their models using regular Python objects. Flax NNX is an evolution of the previous Flax Linen API.

RFIClean excises periodic RFI (broadband as well as narrow-band) in the Fourier domain, and then mitigates narrow-band spectral line RFI as well as broadband bursty time-domain RFI using robust statistics. Primarily designed to efficiently search and mitigate periodic RFI from GMRT time-domain data, RFIClean has evolved to mitigate any spiky (in time or frequency) RFI as well, and from any SIGPROC filterbank format data file. RFIClean uses several modules from SIGPROC (ascl:1107.016) to handle the filterbank format I/O.

PDQ predicts the positions on the sky of high-redshift quasars that should provide photons that are both acausal and uncorrelated. The predicted signal-to-noise ratios are calculated at framerate sufficient for random-number generation input to a loophole-free Bell test, and are calibrated against a public archival dataset of four pairs of highly-separated bright stars observed simultaneously (and serendipitously) at 17 Hz with that same instrumentation in 2019 to 2021.

AstroPT trains astronomical large observation models using imagery data. The code follows a similar saturating log-log scaling law to textual models and the models' performances on downstream tasks as measured by linear probing improves with model size up to the model parameter saturation point. Other modalities can be folded into the AstroPT model, and use of a causally trained autoregressive transformer model enables integration with the wider deep learning FOSS community.

MultiREx generates synthetic transmission spectra of exoplanets. This tool extends the functionalities of the TauREx (ascl:2209.015) framework, enabling the mass production of spectra and observations with added noise. Though the package was originally conceived to train machine learning models in the identification of biosignatures in noisy spectra, it can also be used for other purposes.

GalClass facilitates visual morphological classifications of large samples of galaxies taking advantage of multi-wavelength imaging and ancillary information. It offers a versatile Graphic User Interface (GUI), which adapts to the provided classification scheme. It displays a series of pre-prepared PDF files for classification, grouping by galaxy and filter, while also listing relevant metadata and displaying a color image of each source. GalClass enables easy navigation through the sample and continuously outputs classification results in a JSON file. Finally, it offers an analysis submodule which combines and processes output files of multiple classifications.

ChromaStarPy computes the vertical structure of a static, plane-parallel, one-dimensional stellar atmosphere in local thermodynamic equilibrium (LTE); it also computes the emergent spectrum incorporating opacity computed with a comprehensive atomic line list from the NIST Atomic Spectra Database. The code provides post-processed data products that are ready to visualize in a Python IDE such as spyder. ChromaStarPy is a port of ChromaStarServer (ascl:1701.009); the code enables users to experiment with and develop a stellar astrophysical modeling code in a graphical IDE, and to compare observational data to ad hoc model output.

ExoInt devolatilizes stellar abundances to produce rocky exoplanetary bulk composition to constrain the modeling of the exoplanet interiors; the code uses Monte Carlo simulations that assume that each element’s abundance (within its uncertainty) follows a Gaussian distribution. ExoInt also contains a module to provide the mineralogy based on the stoichemitric output of mantle and core compositions, core mass fraction, along with the given mass and radius information.

tpfplotter creates a TESS Target Pixel File of a source, overplotting the aperture mask used by the SPOC pipeline and the Gaia catalog to check for possible contaminations within the aperture. The software can create 1-column paper-ready figures, overplotting the Gaia DR2 catalog to the TESS Target Pixel Files, and can create plots for any target observed by TESS. tpfplotter can search by coordinates if the TIC number of the source is not known.

Pytmosph3R computes transmission and emission spectra based on 3D atmospheric simulations, for example, performed with the LMDZ generic global climate model. It produces transmittance maps of the atmospheric limb at all wavelengths that can then be spatially integrated to yield the transmission spectrum. Pytmosph3R can use 3D time-varying atmospheric structures from a GCM as well as simpler, parameterized 1D or 2D structures, and can be used in notebooks or on the command line.

TROPF (Tidal Response Of Planetary Fluids) enables efficient terrestrial fluid tidal studies across a wide range of parameter space. The software includes several different solutions to the governing equations in classical tidal theory, and can calculate millions of such solutions on several-minute-long timescales. Written in MATLAB/Octave, TROPF can be ported to Python and other languages, as the instructions for building the operator matrices are described in detail and the coding of core TROPF routines adheres to generic sparse matrix operations and avoids functions specific to MATLAB.

DYNAMITE (DYNAmical Multi-planet Injection TEster) predicts the presence of unseen planets in multi-planet systems via population statistics. The code uses the specific (yet often incomplete) data on the orbital and physical parameters for the planets in any given system's architecture and combines it with detailed statistical population models and a dynamical stability criterion to predict the likelihood for the parameters of one additional planet in the system. DYNAMITE's predictions are given in the form of observable values (transit depth measurements, RV semi-amplitudes, or direct imaging separation and contrast), which can be tested by follow-up observations.