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Physical Properties of 6.7 Million Galaxies from the DESI Bright Galaxy Survey: Spectral Fitting and Systematic Tests with Mock Spectra
Niu Li, Hu Zou, Jinfu Gou, Weijian Guo, Wenxiong L, Haoming Song, Jipeng Sui, Xi Tan, Yunao Xiao, Jingyi Zhang
https://arxiv.org/abs/2607.19162 https://arxiv.org/pdf/2607.19162 https://arxiv.org/html/2607.19162
arXiv:2607.19162v1 Announce Type: new
Abstract: We present a comprehensive analysis of the physical properties of galaxies in the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) Bright Galaxy Survey (BGS), based on full spectral fitting of $\sim 6.7$ million galaxy spectra. Using a customized spectral fitting pipeline, we derive key physical parameters including stellar mass, stellar velocity dispersion, stellar population age, dust attenuation, and emission-line properties. To quantify the reliability and systematic uncertainties of our measurements, we construct a large set of mock spectra that closely reproduce the observed properties of DESI data, including realistic noise and spectral features. By comparing the recovered parameters with the known inputs, we assess the performance of the spectral fitting as a function of stellar continuum signal-to-noise ratio (S/N, defined as the ratio of the median continuum flux to its associated error) and redshift. We find that stellar masses can be robustly recovered with negligible bias for spectra with $\mathrm{S/N} \gtrsim 5$, while low-S/N spectra ($\mathrm{S/N} \lesssim 5$) show a mild systematic overestimation of $\sim 0.1$ dex and increased scatter. Similar trends are observed for stellar population parameters, while emission-line fluxes are recovered with high accuracy and minimal bias. We further validate our stellar mass estimates by comparison with independent measurements from photometric spectral energy distribution fitting, finding good overall consistency within the expected systematic uncertainties. The value-added catalog presented in this work enables a wide range of statistical studies of galaxy evolution with DESI, and provides a foundation for future analyses.
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