CUBE2 is a successor to CUBE (ascl:1805.018), a cosmological N-body simulation code emphasizing memory efficiency, computational performance, scalability, and precision. It employs a 3-level particle-mesh (PM) and particle-particle (PP) method to compute gravitational forces, where the resolution of the innermost PM layer and the PP force range are adaptive according to the clustering of the matter distribution to minimize computation time. Force accuracy is enhanced using optimized Green's functions. The code achieves high weak and strong scalability.

dalip-estimator is a lightweight Python tool that provides a fast empirical estimate of effective gravitational acceleration directly from gravitational lensing observables. The software implements a simple algebraic relation between light-deflection angle and impact parameter, allowing rapid exploratory calculations without requiring detailed mass modeling or iterative fitting. It is intended for educational use, quick diagnostic estimates, and preliminary analysis workflows in gravitational lensing studies.

Detailed studies of exoplanet and brown dwarf atmospheres rely on precise knowledge of the spectral features of possible atmospheric species. These features are a result of the interaction of different molecules and atoms with the radiation of the host star or the intrinsic thermal radiation of the object. The interaction is described by the opacity of a species, which determines how much light is absorbed at a given wavelength. The strength and shape of these opacities are very dependent on the temperature and pressure of the atmosphere, which is why they have to be calculated for a wide range of conditions. line racer is a Python package that enables computing high-resolution opacities from large molecular line lists in an effective manner. It offers users a wide range of options to customize opacity calculations to their needs and available hardware. The code is designed for efficient parallelization on multi-core and multi-node systems and can produce outputs compatible with popular atmospheric modeling and retrieval tools.

Neloura is a web-based astronomical image analysis and visualization platform designed for multi-wavelength data exploration. The software provides browser-based viewing of FITS images, catalog overlay, source detection, photometry, and generation of publication-quality figures. Neloura supports large astronomical datasets through tile-based rendering and enables interactive analysis across HST, JWST, and ALMA observations.

socca (Source Characterization using a Composable Analysis) is a Python library for modeling astronomical image data using forward modeling and Bayesian inference. The package is designed around modular, composable building blocks that allow users to flexibly define complex source models with hierarchical and physically motivated priors, instrument responses, and noise models within a unified framework.
socca leverages JAX to enable automatic differentiation, just-in-time compilation, and efficient vectorized computations, making it well suited for computationally intensive inference tasks. Posterior exploration is supported through state-of-the-art sampling algorithms, enabling scalable parameter estimation.

Light-GG is a lightweight, unit-agnostic Python utility for constructing
two-dimensional coordinate grids using one-dimensional axes and NumPy
broadcasting. It enables memory-efficient grid construction from
human-intuitive inputs (reference point, extent, resolution) while avoiding
explicit 2D meshgrids. The package is intentionally minimal and domain-neutral,
and is suitable for prototyping in astronomy and other scientific applications.

LIGHT implements a Bayesian framework for reconstructing the three-dimensional galaxy and underlying dark matter fields informed by large-scale structure. The software uses Hamiltonian Monte Carlo (HMC) sampling via NumPyro (ascl:2505.005) to infer the true galaxy density distribution and its magnitude distribution given incomplete galaxy catalog data, addressing biases in statistical analyses that combine galaxy catalogs with other observations. LIGHT’s models incorporate spatial correlations of galaxies on cosmological scales and enable construction of improved three-dimensional priors in redshift and sky position for host galaxies of gravitational-wave events. LIGHT supports dark siren cosmology, where gravitational-wave distances are combined with reconstructed galaxy fields to improve inference of cosmological parameters, and includes example configurations and scripts.

Cesam2k20 computes one-dimensional stellar structure and evolution models under the assumption of hydrostatic equilibrium. The code evolves stellar models from pre-main sequence through advanced evolutionary stages using modular numerical schemes and up-to-date microphysics. Cesam2k20 builds on the CESAM (ascl:1010.059) stellar evolution code and incorporates developments from related tools such as ADIPLS (ascl:1109.002) for asteroseismic applications. It includes treatments of processes such as chemical mixing, angular momentum transport, and rotation within a unified framework. Cesam2k20 supports stellar modeling studies in both classical and asteroseismic contexts.

MeerFish implements a Fisher forecasting framework tailored to upcoming large-sky single-dish HI intensity mapping surveys such as those planned with the MeerKAT telescope and its successor within the SKAO project. The code models survey specifications and expected statistical uncertainties to compute Fisher matrices and forecast parameter constraints on cosmological observables derived from 21 cm intensity maps. It provides modules to define survey configurations, cosmological and nuisance parameters, and the associated signal and noise models needed for forecasting survey performance. MeerFish outputs predicted errors on power spectrum measurements and derived parameters, enabling quantitative assessment of survey design choices and scientific reach.

Duffell_Gap implements a one-dimensional model for generating synthetic gap profiles carved by planets in protoplanetary disks. The package includes C and Python reference implementations that compute density profiles in a disk perturbed by an embedded planet, with disk and planet parameters set directly in the model code. Users can compile and run the C version to generate output gap profiles and inspect the resulting disk structure, and adapt the Python version as starter code for further development. The software is intended as a straightforward base implementation of the gap model that can be modified for specialized studies of planet–disk interactions.