The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has acquired tens of millions of low-resolution spectra of stars. This paper investigated the parameter estimation problem for these spectra. To this end, we proposed a deep learning model StarGRU network (StarGRUNet). This network was further applied to estimate the stellar atmospheric physical parameters and 13 elemental abundances from LAMOST low-resolution spectra. On the spectra with signal-to-noise ratios greater than or equal to 5, the estimation precisions are 94 K and 0.16 dex on Teff and logg respectively, 0.07 dex to 0.10 dex on [C/H], [Mg/H], [Al/H], [Si/H], [Ca/H], [Ni/H] and [Fe/H], and 0.10 dex to 0.16 dex on [O/H], [S/H], [K/H], [Ti/H] and [Mn/H], and 0.18 dex and 0.22 dex on [N/H] and [Cr/H] respectively. The model shows advantages over available models and high consistency with high-resolution surveys. We released the estimated catalog computed from about 8.21 million low-resolution spectra in LAMOST DR8, code, trained model, and experimental data for astronomical science exploration and data processing algorithm research respectively.
@article{arxiv.2303.15690,
title = {Estimating Stellar Parameters from LAMOST Low-resolution Spectra},
author = {Xiangru Li and Boyu Lin},
journal= {arXiv preprint arXiv:2303.15690},
year = {2023}
}