论文标题
SPCANET：Lamost-II中等分辨率调查的恒星参数和化学丰度网络
SPCANet: Stellar Parameters and Chemical Abundances Network for LAMOST-II Medium Resolution Survey
论文作者
论文摘要
基本恒星大气参数T_EFF和LOOG G和13个化学丰度，用于使用深度学习方法的LAMOST中等分辨率调查（MRS）数据集的中等分辨率光谱。我们设计的神经网络被称为Spcanet，精确地将Lamost MRS Spectra绘制为出色的参数和化学丰度。 Spcanet得出的恒星标签的精度为T_EFF的精确度为119 K，对数g的精度为0.17 DEX。 11个要素的丰度精度，包括[C/H]，[N/H]，[O/H]，[Mg/H]，[Al/H]，[Si/H]，[Si/H]，[S/H]，[CA/H]，[Ti/H]，[Ti/H]，[Cr/H]，[Cr/H]，[Fe/H]，[Fe/H]，[Fe/H]，以及[Ni/H]，以及[Ni/H]是0.06〜0.12 DEX DEX，同时[CU]，同时[CU]，同时[Cu s]，cu cu n;即使对于低至10的光谱，SPCANET的结果也与其他调查的结果（例如Apogee，Galah和Rave）一致，也可以达到这些精度。估计参数的目录可在\ url {http://paperdata.china-vo.org/lamost/mrs_parameters_elements.csv}获得。
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{http://paperdata.china-vo.org/LAMOST/MRS_parameters_elements.csv}.
