The RUN Pipeline first classifies strong lenses with a ResNet model (first Github repo below), and further detects the locations of small Einstein radius systems in cutout images down to θ_E∼0.03″ with a U-Net model (second Github repo below). A detailed description is provided in the paper "ML-Driven Strong Lens Discoveries: Down to θ_E∼0.03″ and Mhalo

adstex automatically identifies all citation keys in a TeX source file and builds the corresponding bibliography file (.bib file) by fetching the reference information from NASA's Astrophysics Data System (ADS). adstex recognizes all variants of the cite commands in TeX, and works with various styles of citation keys, including arXiv IDs, DOIs, and ADS bibcodes. When a citation key is in the format of first-author name and year, adstex will query NASA's ADS and return a list of possible reference matches for the user to select the intended one. When a reference entry has updated information on NASA's ADS, adstex can detect such changes and fetch the new information and update the user's bibliography file. adstex supports any reference entry that is available on NASA's ADS, and allows the authors to write papers without manually searching for the bibliography entries.

IAR_Model fits unequally spaced time series from the Irregular Autoregressive (IAR). Available as Python and R functions, IAR_Model can generate observations for each process, compute the negative of the log likelihood of these process, fit each model to irregularly sampled data, and test the significance of the estimate.

fm4ar (flow matching for atmospheric retrievals) infers atmospheric properties of exoplanets from observed spectra. It uses flow matching posterior estimation (FMPE) for its machine learning (ML) approach to atmospheric retrieval; this approach provides many of the advantages of neural posterior estimation (NPE) while also providing greater architectural flexibility and scalability. The package uses importance sampling (IS) to verify and correct ML results, and to compute an estimate of the Bayesian evidence. fm4ar's ML models are conditioned on the assumed noise level of a spectrum (i.e., error bars), thus making them adaptable to different noise models.

AGNI simulates the atmospheric temperature-, height-, and compositional-structures of atmospheres overlying magma oceans while ensuring that radiative-convective equilibrium is maintained throughout the atmosphere. The code also supports real gas equations of state, self-gravitation, and various spectral surface compositions. Accounting for these energy transport processes permits AGNI to calculate atmospheric structure, which also yields realistic cooling rates for young rocky planets with magma oceans.

FiCUS (FItting the stellar Continuum of Uv Spectra) fit the stellar continuum of extragalactic ultraviolet (UV) spectra. The code takes observed-frame wavelength, flux density (with errors) and user-defined mask arrays as inputs, and returns an estimation of the galaxy stellar age, metallicity and dust extinction, as well as other secondary Spectral Energy Distribution (SED) parameters. FiCUS has two scripts; the first reads the INPUT file provided by the user and performs the fit according to selected options. It then gives the best-fit parameters and creates the OUTPUT files and figures. The second script includes pre-defined routines for spectral analysis, loading INPUT files and handling with data and models, as well as functions for the fitting routine, SED-parameters calculations and plotting, and imports functions into the first script.

pyStarburst99 is a Python version of the Starburst99 (ascl:1104.003) population synthesis code for star-forming galaxies. This Python version includes new evolutionary tracks and synthetic spectral energy distributions. pyStarburst99 provides wider coverage in metallicity, mass, and resolution, and includes evolutionary and spectral models of stars up to 300–500 M⊙.

The SIGWAY data analysis pipeline computes second-order, scalar induced gravitational wave signals emitted by curvature perturbations in the early universe. The package solves the Mukhanov-Sasaki equation for single field ultra-slow roll inflationary models and computes the primordial scalar power spectrum P_ζ. SIGWAY also computes the second order gravitational wave power spectrum Ω_GW from P _ζ for reentry during radiation domination or a phase of early matter domination.

sMV (serial MultiView) scripts provide a semi-automatic and easy-to-use workflow for serial MultiView phase plane estimation. The phase plane is iteratively rotated based on the time series of calibrator residual phases; because time-domain information is included in the iterations, phase ambiguities are accurately and automatically identified. sMV enables efficient, high-accuracy differential astrometry and artifact-reduced imaging for astrophysical studies.

Built on Flax (ascl:2504.026), DeepSSM emulates gravitational wave (GW) spectra produced by sound waves during cosmological first-order phase transitions in the radiation-dominated era. It uses neural networks trained on an enhanced version of the Sound Shell Model (SSM). The code provides instantaneous predictions of GW spectra given the phase transition parameters, while achieving agreement with the enhanced SSM model. DeepSSM is particularly suitable for direct Bayesian inference on phase transition parameters without relying on empirical templates, such as broken power-law models.