Galmoss: A package for GPU-accelerated galaxy profile fitting

Chen, Mi, Souza, Rafael S. de, Xu, Quanfeng, Shen, Shiyin, Chies-Santos, Ana L., Ye, Renhao, Canossa-Gosteinski, Marco A. and Cong, Yanping (2024) Galmoss: A package for GPU-accelerated galaxy profile fitting. Astronomy and Computing, 47: 100825. pp. 1-11. ISSN 2213-1337

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We introduce galmoss, a python-based, torch-powered tool for two-dimensional fitting of galaxy profiles. By seamlessly enabling GPU parallelization, galmoss meets the high computational demands of large-scale galaxy surveys, placing galaxy profile fitting in the CSST/LSST-era. It incorporates widely used profiles such as the Sérsic, Exponential disk, Ferrer, King, Gaussian, and Moffat profiles, and allows for the easy integration of more complex models. Tested on 8289 galaxies from the Sloan Digital Sky Survey (SDSS) g-band with a single NVIDIA A100 GPU, galmoss completed classical Sérsic profile fitting in about 10 min. Benchmark tests show that galmoss achieves computational speeds that are 6 × faster than those of default implementations.

Item Type	Article
Additional information	© 2024 The Author(s). Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/
Keywords	gpu computing, machine and deep learning, extragalactic astronomy, galaxies, data analysis – methods, statistical – gpu computing, general – methods, astronomy and astrophysics, computer science applications, space and planetary science
Date Deposited	15 May 2025 15:31
Last Modified	15 May 2025 15:31

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