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Elemental abundances distributions in (R, $V_{\phi}$) plane with LAMOST DR5 and Gaia DR2

Astrophysics of Galaxies · Submitted 17 Nov 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our results extend the chemical characterization of the ridges in the (R, $V_{\phi}$) plane to about $R = $13 kpc toward the anticenter direction.

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arXiv ID1911.07150 (version 1)
Pith IDX6UHUYYW, Pith's permanent record of this paper and its review
CitationGang Zhao, Jiajun Zhang, Jia Zhu, Jingkun Zhao, Wenbo Zuo, Xilong Liang, Yuqin Chen. "Elemental abundances distributions in (R, $V_{\phi}$) plane with LAMOST DR5 and Gaia DR2." arXiv:1911.07150 (2019). https://pith.science/paper/X6UHUYYW
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 8,743 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.11 · human participants 0.02 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.19 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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 $T_{\textrm{eff}}$, log $g$, metallicity ([M/H] and [Fe/H]) and [$\alpha$/M] for 1 063 386 stars have been estimated. Then those stars were cross matched with Gaia DR2 data to obtain kinematic parameters. We presented distributions of chemical abundances in the $V_{\phi}$ versus \textit{R} coordinate. Our results extend the chemical characterization of the ridges in the (R, $V_{\phi}$) plane to about $R = $13 kpc toward the anticenter direction. In addition, radial elemental abundance gradients for disk stars with abs(\textit{z})$ < 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 $\pm $0.0015 for $R < 13.09$ kpc and -0.0173 $\pm $0.0028 for $R < 13.09$ kpc. Gradients for other elemental abundances are also obtained, for example, [$\alpha$/M] gradients for disk stars is 0.0030 $\pm $0.0002.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 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. background 1.00 0.05 0.02 asserted 1.00
s1 Both kinematic information and stellar elemental abundances are needed to reveal their evolution history. background 0.87 0.06 0.03 asserted 1.00
s2 We have used labels from the APOGEE survey to predict elemental abundances for a huge amount of low resolution spectra from the LAMOST survey. method 0.91 0.95 0.03 asserted 1.00
s3 Deep learning with artificial neural networks can automatically draw on physically sensible features in the spectrum for their predictions. method 0.85 0.14 0.02 asserted 0.94
s4 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 $T_{\textrm{eff}}$, log $g$, metallicity ([M/H] and [Fe/H]) and [$\alpha$/M] for 1 063 386 stars have been estimated. contribution 0.47 0.92 0.05 asserted 1.00
s5 Then those stars were cross matched with Gaia DR2 data to obtain kinematic parameters. method 1.00 0.91 0.02 asserted 1.00
s6 We presented distributions of chemical abundances in the $V_{\phi}$ versus \textit{R} coordinate. contribution 0.55 0.96 0.12 asserted 1.00
s7 Our results extend the chemical characterization of the ridges in the (R, $V_{\phi}$) plane to about $R = $13 kpc toward the anticenter direction. result 0.91 0.95 0.43 asserted 1.00
s8 In addition, radial elemental abundance gradients for disk stars with abs(\textit{z})$ < 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. contribution 0.65 0.96 0.10 asserted 1.00
s9 The radial metallicity gradients for disk stars are respectively -0.0475 $\pm $0.0015 for $R < 13.09$ kpc and -0.0173 $\pm $0.0028 for $R < 13.09$ kpc. result 1.00 0.92 0.16 asserted 1.00
s10 Gradients for other elemental abundances are also obtained, for example, [$\alpha$/M] gradients for disk stars is 0.0030 $\pm $0.0002. result 1.00 0.94 0.09 asserted 1.00

What kind of paper the classifier reports this is: empirical study 0.74 · evidence from analysis of existing data 1.00.

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The paper
Original 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 $T_{\textrm{eff}}$, log $g$, metallicity ([M/H] and [Fe/H]) and [$\alpha$/M] for 1 063 386 stars have been estimated. Then those stars were cross matched with Gaia DR2 data to obtain kinematic parameters. We presented distributions of chemical abundances in the $V_{\phi}$ versus \textit{R} coordinate. Our results extend the chemical characterization of the ridges in the (R, $V_{\phi}$) plane to about $R = $13 kpc toward the anticenter direction. In addition, radial elemental abundance gradients for disk stars with abs(\textit{z})$ < 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 $\pm $0.0015 for $R < 13.09$ kpc and -0.0173 $\pm $0.0028 for $R < 13.09$ kpc. Gradients for other elemental abundances are also obtained, for example, [$\alpha$/M] gradients for disk stars is 0.0030 $\pm $0.0002.

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