http://arxiv.org/abs/2001.03470
The fundamental stellar atmospheric parameters T_eff and log g and 13 chemical abundances are derived for medium-resolution spectroscopy from LAMOST Medium-Resolution Survey (MRS) data sets with a deep-learning method. The neural networks we designed, named as SPCANet, precisely map LAMOST MRS spectra to stellar parameters and chemical abundances. The stellar labels derived by SPCANet are with precisions of 119 K for T_eff and 0.17 dex for log g. The abundance precision of 11 elements including [C/H], [N/H], [O/H], [Mg/H], [Al/H], [Si/H], [S/H], [Ca/H], [Ti/H], [Cr/H], [Fe/H], and [Ni/H] are 0.06~0.12 dex, while of [Cu/H] is 0.19 dex. These precisions can be reached even for spectra with signal-to-noise as low as 10. The results of SPCANet are consistent with those from other surveys such as APOGEE, GALAH and RAVE, and are also validated with the previous literature values including clusters and field stars. The catalog of the estimated parameters is available at \url{this http URL}.
R. Wang, A. Luo, J. Chen, et. al.
Mon, 13 Jan 20
6/61
Comments: Accepted by ApJ, 16 pages, 14 figures, 2 tables