Estimating stellar parameters from LAMOST low-resolution spectra

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has acquired tens of millions of low-resolution spectra of stars. This paper investigates the parameter estimation problem for these spectra. To this end, we propose the deep learning model StarGRU network (StarGRUNet). This network is 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 T-eff and log g respectively, 0.07 to 0.10 dex on [C/H], [Mg/H], [Al/H], [Si/H], [Ca/H], [Ni/H] and [Fe/H], 0.10 to 0.16 dex on [O/H], [S/H], [K/H], [Ti/H] and [Mn/H], and 0.18 and 0.22 dex on [N/H] and [Cr/H]. The model shows advantages over other available models and high consistency with high-resolution surveys. We released the estimated catalogue 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.

Automated summary

This study proposes a deep learning model called StarGRUNet to estimate stellar atmospheric physical parameters and elemental abundances from LAMOST low-resolution spectra. The model shows high estimation precisions and consistency with high-resolution surveys. The estimated catalogue, code, trained model, and experimental data have been released for astronomical science exploration and data processing algorithm research.