HaloGraphNet predicts halo masses from simulations using Graph Neural Networks. Given a dark matter halo and its galaxies, this software creates a graph with information about the 3D position, stellar mass and other properties. It then trains a Graph Neural Network to predict the mass of the host halo. Data are taken from the CAMELS hydrodynamic simulations.

RealSim generates survey-realistic synthetic images of galaxies from hydrodynamical simulations of galaxy formation and evolution. The main functionality of this code inserts "idealized" simulated galaxies into Sloan Digital Sky Survey (SDSS) images in such a way that the statistics of sky brightness, resolution, and crowding are matched between simulated galaxies and observed galaxies in the SDSS. The suite accepts idealized synthetic images in calibrated AB surface brightnesses and rebins them to the desired redshift and CCD angular scale; RealSim can add Poisson noise, if desired, by adopting generic values of photometric calibrations in survey fields. Images produced by the suite can be inserted into real image fields to incorporate real skies, PSF degradation, and contamination by neighboring sources in the field of view. The RealSim methodology can be applied to any existing galaxy imaging survey.