Galmoss: A package for GPU-accelerated galaxy profile fitting

Mi Chen, Rafael S. de Souza, Quanfeng Xu, Shiyin Shen, Ana L. Chies-Santos, Renhao Ye, Marco A. Canossa-Gosteinski, Yanping Cong

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Abstract

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.
Original languageEnglish
Article number100825
Pages (from-to)1-11
Number of pages11
JournalAstronomy and Computing
Volume47
Early online date16 Apr 2024
DOIs
Publication statusPublished - 22 Apr 2024

Keywords

  • GPU computing
  • machine and deep learning
  • Extragalactic astronomy
  • Galaxies
  • Data analysis – methods
  • Statistical – GPU computing
  • General – methods

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