Galmoss: A package for GPU-accelerated Galaxy Profile Fitting

Fuente: arXiv

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Bibliographische Detailangaben
Hauptverfasser:	Chen, Mi, de Souza, Rafael S., Xu, Quanfeng, Shen, Shiyin, Chies-Santos, Ana L., Ye, Renhao, Canossa-Gosteinski, Marco A., Cong, Yanping
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Astrophysics of Galaxies
Online-Zugang:	Acceder al recurso
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author	Chen, Mi de Souza, Rafael S. Xu, Quanfeng Shen, Shiyin Chies-Santos, Ana L. Ye, Renhao Canossa-Gosteinski, Marco A. Cong, Yanping
author_facet	Chen, Mi de Souza, Rafael S. Xu, Quanfeng Shen, Shiyin Chies-Santos, Ana L. Ye, Renhao Canossa-Gosteinski, Marco A. Cong, Yanping
contents	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 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 8,289 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 minutes. Benchmark tests show that galmoss achieves computational speeds that are 6 $\times$ faster than those of default implementations.
format	Preprint
id	arxiv_https___arxiv_org_abs_2404_07780
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Galmoss: A package for GPU-accelerated Galaxy Profile Fitting Chen, Mi de Souza, Rafael S. Xu, Quanfeng Shen, Shiyin Chies-Santos, Ana L. Ye, Renhao Canossa-Gosteinski, Marco A. Cong, Yanping Astrophysics of Galaxies 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 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 8,289 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 minutes. Benchmark tests show that galmoss achieves computational speeds that are 6 $\times$ faster than those of default implementations.
title	Galmoss: A package for GPU-accelerated Galaxy Profile Fitting
topic	Astrophysics of Galaxies
url	https://arxiv.org/abs/2404.07780

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