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. It 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.

LeR calculates detectable rates of gravitational waves events (both lensed and un-lensed events). Written in Python, it performs statistical simulation and forecasting of gravitational wave (GW) events and their rates. The code samples gravitational wave source properties and lens galaxies attributes and source redshifts, and can generate image properties such as source position, magnification, and time delay. The package also calculates detectable merger rates per year. Key features of LeR include efficient sampling, optimized SNR calculations, and systematic archiving of results. LeR is tailored to support both GW population study groups and GW lensing research groups by providing a comprehensive suite of tools for GW event analysis.

R2D2 (Residual-to-Residual DNN series for high-Dynamic range imaging) performs synthesis imaging for radio interferometry. The R2D2 algorithm takes a hybrid structure between a Plug-and-Play (PnP) algorithm and a learned version of the well-known Matching Pursuit algorithm. Its reconstruction is formed as a series of residual images, iteratively estimated as outputs of iteration-specific Deep Neural Networks (DNNs), each taking the previous iteration’s image estimate and associated back-projected data residual as inputs. The primary application of the R2D2 algorithm is to solve large-scale high-resolution high-dynamic range inverse problems in radio astronomy, more specifically 2D planar monochromatic intensity imaging.

SCONE (Supernova Classification with a Convolutional Neural Network) classifies supernovae (SNe) by type using multi-band photometry data (lightcurves) using a convolutional neural networks. SCONE takes in supernova (SN) photometry data in the format output by SNANA simulations, separated into two types of files: metadata and observation data. Photometric data is pre-processed via 2D Gaussian process regression, which smooths over irregular sampling rates between filters and also allows SCONE to be independent of the filter set on which it was trained.

NRPyElliptic sets up initial data (ID) for numerical relativity (NR) using the same numerical methods employed for solving hyperbolic evolution equations. The code implements a hyperbolic relaxation method to solve complex nonlinear elliptic PDEs for NR ID. The hyperbolic PDEs are evolved forward in (pseudo)time, resulting in an exponential relaxation of the arbitrary initial guess to a steady state that coincides with the solution of the elliptic system. The package solves these equations on highly efficient numerical grids exploiting underlying symmetries in the physical scenario. NRPyElliptic is built in the NRPy+ (ascl:1807.025) framework, which facilitates the solution of hyperbolic PDEs on Cartesian-like, spherical-like, cylindrical-like, or bispherical-like numerical grids.

Wavelets are a powerful mathematical tool whose most celebrated applications are the analysis, compression and de-noising of scientific data. JOFILUREN is a wavelet code designed for data analysis and de-noising applications. It is written in Fortran, is portable, and can also be used for studying and reducing the physical effects of particle noise in particle-mesh computer simulations. JOFILUREN is introduced in Paper I, is described in Paper II, and is further discussed in Paper III, as referenced below.

* Paper I: Romeo A. B., Horellou C. and Bergh J. (2003), "N-Body Simulations with Two-Orders-of-Magnitude Higher Performance Using Wavelets", Monthly Notices of the Royal Astronomical Society 342, 337-344
https://ui.adsabs.harvard.edu/abs/2003MNRAS.342..337R/abstract

* Paper II: Romeo A. B., Horellou C. and Bergh J. (2004), "A Wavelet Add-On Code for New-Generation N-Body Simulations and Data De-Noising (JOFILUREN)", Monthly Notices of the Royal Astronomical Society 354, 1208-1222
https://ui.adsabs.harvard.edu/abs/2004MNRAS.354.1208R/abstract

* Paper III: Romeo A. B., Agertz O., Moore B. and Stadel J. (2008), "Discreteness Effects in ΛCDM Simulations: A Wavelet-Statistical View", The Astrophysical Journal 686, 1-12
https://ui.adsabs.harvard.edu/abs/2008ApJ...686....1R/abstract

Supplementary information is given in the readme file of JOFILUREN. A pedagogical introduction to wavelets and wavelet applications, containing several useful videos and lecture notes, is given here:
https://fy.chalmers.se/~romeo/RRY025/notes+videos/

Gradus.jl traces geodesics and calculates observational signatures of accreting compact objects. The code requires only a specification of the non-zero metric components of a chosen spacetime in order to solve the geodesic equation and compute a wide variety of trajectories and orbits. Gradus includes algorithms for calculating physical quantities are implemented generically, so they may be used with different classes of spacetime with minimal effort.

Colume (COLUMn to vOLUME) uses the statistical and spatial distribution of a column density map to infer a likely volume density distribution along each line of sight. The Python package incorporates all pre-processing (in particular re-sampling) functions needed to efficiently work on the column density maps. Colume's outputs are saved in Numpy format.

XGPaint, written in Julia, generates maps of extragalactic foregrounds, using astrophysical models designed to replicate the statistics of the millimeter sky. The code computes simulated galaxies from the Cosmic Infrared Background (CIB), radio galaxies, and contributions and distortions from the Sunyaev-Zeldovich (SZ) effect. XGPaint is multithreaded, and supports both HEALPix and Plate Carrée pixelizations.

The Blooming Tree (BT) algorithm identifies clusters, groups, and substructures from galaxy redshift surveys. Based on the hierarchical clustering method, it takes the projected binding energy as the linking length and provides three main analysis approaches to trim the hierarchical tree; 1.) the direct trimming (binding energy, velocity disperion, or eta); 2.) the sigma plateau, when no trimming threshold is appointed; and 3.) the blooming tree. The Blooming Tree algorithm tool works only in the terminal.