TY - JOUR
T1 - Galmoss: A package for GPU-accelerated galaxy profile fitting
AU - Chen, Mi
AU - Souza, Rafael S. de
AU - Xu, Quanfeng
AU - Shen, Shiyin
AU - Chies-Santos, Ana L.
AU - Ye, Renhao
AU - Canossa-Gosteinski, Marco A.
AU - Cong, Yanping
N1 - © 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/
PY - 2024/4/22
Y1 - 2024/4/22
N2 - 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.
AB - 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.
KW - GPU computing
KW - machine and deep learning
KW - Extragalactic astronomy
KW - Galaxies
KW - Data analysis – methods
KW - Statistical – GPU computing
KW - General – methods
UR - http://www.scopus.com/inward/record.url?scp=85190785726&partnerID=8YFLogxK
U2 - 10.1016/j.ascom.2024.100825
DO - 10.1016/j.ascom.2024.100825
M3 - Article
SN - 2213-1337
VL - 47
SP - 1
EP - 11
JO - Astronomy and Computing
JF - Astronomy and Computing
M1 - 100825
ER -