GalaxyGenius: Mock galaxy image generator for various telescopes from hydrodynamical simulations

¹ National Astronomical Observatory, Chinese Acedemy of Science, Beijing 100101, China
² Science Center for China Space Station Telescope, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China
³ Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
⁴ Shanghai Key Lab for Astrophysics, Shanghai Normal University, Shanghai 200033, China
⁵ Purple Mountain Observatory, Chinese Academy of Sciences, Jiangsu 210023, China
⁶ Department of Physics, Guangdong Technion – Israel Institute of Technology, Guangdong 515063, China
⁷ Centre for Space Research, North-West University, Potchefstroom 2520, South Africa
⁸ Department of Physics and Electronics, Adekunle Ajasin University, P. M. B. 001, Akungba-Akoko, Ondo State, Nigeria
⁹ Universitäts-Sternwarte München, Fakultät für Physik, LudwigMaximilians-Universität München, Scheinerstrasse 1, 81679 München, Germany

^★ Corresponding author: nan.li@nao.cas.cn

Received: 27 February 2025
Accepted: 12 June 2025

Abstract

Aims. We introduce GalaxyGenius, a Python package designed to produce synthetic galaxy images tailored to different telescopes based on hydrodynamical simulations. Its implementation will support and advance research on galaxies in the era of large-scale sky surveys,

Methods. The package comprises three main modules: data preprocessing, ideal data cube generation, and mock observation. Specifically, the preprocessing module extracts necessary properties of star and gas particles for a selected subhalo from hydrodynamical simulations and creates the execution file for the following radiative transfer procedure. Subsequently, building on the above information, the ideal data cube generation module executes a widely used radiative transfer project, specifically the SKIRT, to perform the SED assignment for each particle and the radiative transfer procedure to produce an IFU-like ideal data cube. Lastly, the mock observation module takes the ideal data cube and applies the throughputs of aiming telescopes, while also incorporating the relevant instrumental effects, point spread functions (PSFs), and background noise to generate the required mock observational images of galaxies.

Results. To showcase the outcomes of GalaxyGenius, we created a series of mock images of galaxies based on the IllustrisTNG and EAGLE simulations for both space and ground-based surveys, spanning ultraviolet (UV) to infrared (IR) wavelength coverage, including CSST, Euclid, HST, JWST, Roman, and HSC.

Conclusions. GalaxyGenius offers a flexible framework to generate mock galaxy images with customizable recipes. These generated images can serve as valuable references for verifying and validating new approaches in astronomical research. They can also serve as training sets for relevant studies using deep learning in cases where real observational data are insufficient.