The toolkit Radio Beam manipulates two-dimensional Gaussian beams associated with radio astronomical data. It extracts beam parameters from FITS headers, creates and edits beam definitions within the Astropy (ascl:1304.002) framework, and performs operations such as convolution, deconvolution, and brightness–temperature unit conversion using the beam area. The package also handles sets of beams for spectral cubes with channel-dependent resolution, identifies smallest common beams in a collection, and overlays beam shapes on Matplotlib plots for visualization.

redrock fits redshifts for spectroscopic data using a spectroperfectionism-based template-fitting approach developed for DESI spectra. Its rrdesi command-line tool analyzes input spectra with standard template sets or with archetype templates constructed from physical spectra combined with Legendre polynomials, solving for template and polynomial coefficients via bounded least-squares at a set of trial redshifts. An additional mode refines fits by selecting nearest-neighbor archetypes in chi-squared space and recombining them with Legendre polynomials, enabling flexible template construction for improved redshift estimation.

ELFO (Emission Line Fitting Optimization) improves emission-line fitting in integral-field spectroscopy data by exploiting spatial correlations between neighboring spectra. It wraps PyQSOFit (ascl:1809.008) to fit each spectrum individually, setting initial parameters from the fits of adjacent spaxels and selecting the most spatially smooth solution from multiple fitting orders. Designed for CSST-IFS data and validated on simulated CSST observations, ELFO enhances Hα emission-line fits in quasar spectra and can be adapted with minor changes to other emission lines and IFS datasets.

Sensipy simulates gamma-ray follow-up observations of time-variable astrophysical sources using a Python toolkit built on top of gammapyn (ascl:1711.014). It calculates differential and integral sensitivity curves from instrument response functions and spectral models, and estimates the exposure time required to detect a source with a given spectrum at a specified significance level. Sensipy supports multiple gamma-ray observatories, provides follow-up utilities and lookup tables for quick assessments, includes extragalactic background light absorption models, and provides workflows that load IRFs, apply EBL attenuation, and generate detectability curves under different observing conditions.

astromorph performs automatic, self-supervised morphological classification of astronomical objects from FITS images using a Python machine learning pipeline. It implements the BYOL (Boot-strap Your Own Latents) framework to learn embeddings without labeled training data, providing a lightweight 2D convolutional network, dataset utilities for FITS filelists, and configurable training and inference scripts. astromorph separates objects by morphology through learned representations, exports embeddings and metadata for downstream analysis, and supports both flexible PyTorch-level use and easier command-line workflows for astronomy image datasets.

CHARM infers halo catalogs from particle-mesh matter fields using auto-regressive multi-stage neural networks. It takes processed density and velocity fields, extracts features with 3D CNNs, and jointly predicts halo positions, masses, and velocities in a Rockstar-like (ascl:1210.008) format. CHARM includes preprocessing scripts for matter fields, trained models that can be applied directly to particle-mesh outputs, and example notebooks that demonstrate building mock halo catalogs from simulation data.

SEAGen (Stretched Equal Area (SEA) Generator) generates spherically symmetric particle distributions with accurate particle densities for use in simulations such as SPH. It implements the stretched equal area (SEA) algorithm to create single shells or full spheres of particles that match arbitrary radial density profiles, including layered structures and additional properties like temperature. SEAGen can be used as a standalone file or installed as a package, and includes examples that demonstrate constructing initial conditions for planetary and other astrophysical models.

PrePostCov computes analytic covariances between multipoles of the pre-reconstruction galaxy power spectrum and the post-reconstruction two-point correlation function. It implements an explicit calculation of the cross-covariance between the pre-reconstruction power spectrum multipoles P_l(k) and the post-reconstruction correlation function multipoles xi_l(r), providing a Python interface that depends only on basic scientific libraries for this core functionality. PrePostConv also assembles the full covariance matrix by combining the P_l(k) and xi_l(r) auto-covariance blocks with the cross term, using external tools to handle the pre-reconstruction power spectrum and post-reconstruction correlation function pieces and offering front-end scripts to measure P(k), compute window matrices, and build complete covariance outputs from survey catalogs.

Effective Zel'dovich approximation mock generator (EZmock) constructs approximate mock catalogs for the large-scale structure of the Universe. It uses the Zel'dovich approximation to generate the dark matter density field at a chosen redshift and an effective bias prescription to populate tracers such as galaxies and quasars. It reproduces the clustering of dark matter halos from N-body simulations with percent-level accuracy down to quasi-linear scales. The software provides a parallel C library with APIs, a Python wrapper, and a standalone C program to build EZmocks, all using shared-memory parallelization and FFT libraries to enable fast production of large mock ensembles.

HOD Optimization Routine (HODOR) fits halo occupation distribution (HOD) models to halo catalogs and is highly adaptable to different datasets, clustering codes, and HOD prescriptions. It includes line-of-sight velocity dispersion in the model. Users can swap in alternative two-point correlation function and power spectrum codes, extend the set of HOD models within a common interface, and plug in different optimizers alongside MultiNest (ascl:1109.006) and PyMultiNest (ascl:1606.005). HODOR creates model catalogs from best-fitting parameters and is distributed with a Docker image that bundles the required dependencies for straightforward deployment, including use on systems such as NERSC.