Elemental abundances distributions in (R, $V_{\phi}$) plane with LAMOST DR5 and Gaia DR2
Xilong Liang, Jingkun Zhao, Yuqin Chen, Wenbo Zuo, Jiajun Zhang, Jia, Zhu, Gang Zhao

TL;DR
This study combines Gaia DR2 and LAMOST data with deep learning to analyze elemental abundance distributions and radial gradients in the Milky Way disk, revealing detailed chemical structures and gradients up to 13 kpc.
Contribution
It presents a large-scale estimation of 12 elemental abundances for over one million stars using neural networks, extending chemical characterization of disk ridges and gradients.
Findings
Radial metallicity gradient of -0.0475 dex/kpc for R < 13.09 kpc
Detected abundance gradients for multiple elements including [$ ext{α}$/M]
Extended chemical analysis of disk ridges to R = 13 kpc
Abstract
Since Gaia DR2 was released, many velocity structures in the disk have been revealed such as large scale ridge-like patterns in the phase space. Both kinematic information and stellar elemental abundances are needed to reveal their evolution history. We have used labels from the APOGEE survey to predict elemental abundances for a huge amount of low resolution spectra from the LAMOST survey. Deep learning with artificial neural networks can automatically draw on physically sensible features in the spectrum for their predictions. Abundances of 12 individual elements: [C/Fe], [N/Fe], [O/Fe], [Mg/Fe], [Al/Fe], [Si/Fe], [S/Fe], [Cl/Fe], [Ca/Fe], [Ti/Fe], [Mn/Fe] and [Ni/Fe] along with basic stellar labels , log , metallicity ([M/H] and [Fe/H]) and [/M] for 1 063 386 stars have been estimated. Then those stars were cross matched with Gaia DR2 data to obtain…
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