Abstract

Abstract Since Gaia DR2 was released, many velocity structures in the disk have been revealed, such as large-scale ridge-like patterns in 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 Large Sky Area Multi-Object Fibre Spectroscopic Telescope 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 T eff, log g, metallicity ([M/H] and [Fe/H]), and [α/M] for 1,063,386 stars have been estimated. Then, those stars were cross-matched with Gaia DR2 data to obtain kinematic parameters. We present distributions of chemical abundances in the V ϕ versus R coordinate. Our results extend the chemical characterization of the ridges in the (R, V ϕ ) plane to about R = 13 kpc toward the anticenter direction. In addition, radial elemental abundance gradients for disk stars with abs(z) < 0.5 kpc are investigated, and we fitted a line for median abundance values of bins of stars with galactocentric distance between R > 7.84 kpc and R < 15.84 kpc. The radial metallicity gradients for disk stars are, respectively, −0.0475 ± 0.0015 for R ≥ 13.09 kpc and −0.0173 ± 0.0028 for R < 13.09 kpc. Gradients for other elemental abundances are also obtained for disk stars: the [α/M] gradient is 0.0030 ± 0.0002; the [Al/Fe] gradient is 0.0030 ± 0.0002; and the [Mn/Fe] gradient is −0.0078 ± 0.0005.

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