The stellar parameters and elemental abundances from low-resolution spectra I: 1.2 million giants from LAMOST DR8

Abstract

As a typical data-driven method, deep learning becomes a natural choice for analysing astronomical data nowadays. In this study, we built a deep convolutional neural network to estimate basic stellar parameters $T{}_{\mathrm{eff}}$, log g, metallicity ([M/H] and [Fe/H]) and [$\alpha$/M] along with nine individual elemental abundances ([C/Fe], [N/Fe], [O/Fe], [Mg/Fe], [Al/Fe], [Si/Fe], [Ca/Fe], [Mn/Fe], [Ni/Fe]). The neural network is trained using common stars between the APOGEE survey and the LAMOST survey. We used low-resolution spectra from LAMOST survey as input, and measurements from APOGEE as labels. For stellar spectra with the signal-to-noise ratio in g band larger than 10 in the test set, the mean absolute error (MAE) is 29 K for $T{}_{\mathrm{eff}}$, 0.07 dex for log g, 0.03 dex for both [Fe/H] and [M/H], and 0.02 dex for [$\alpha$/M]. The MAE of most elements is between 0.02 dex and 0.04 dex. The trained neural network was applied to 1,210,145 giants, including sub-giants, from LAMOST DR8 within the range of stellar parameters 3500 K < $T{}_{\mathrm{eff}}$ < 5500 K, 0.0 dex < log g < 4.0 dex, -2.5 dex < [Fe/H] < 0.5 dex. The distribution of our results in the chemical spaces is highly consistent with APOGEE labels and stellar parameters show consistency with external high-resolution measurements from GALAH. The results in this study allow us to further studies based on LAMOST data and deepen our understanding of the accretion and evolution history of the Milky Way. The electronic version of the value added catalog is available at http://www.lamost.org/dr8/v1.1/doc/vac.

arXiv comment18 pages, 18 figures, 2 tables, accepted for publication in MNRAS

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