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Estimating stellar parameters and identifying very metal-poor stars for low-resolution spectra (R ∼ 200)

Published online by Cambridge University Press:  28 November 2023

Tianmin Wu Open the ORCID record for Tianmin Wu [Opens in a new window] ,
Yude Bu Open the ORCID record for Yude Bu [Opens in a new window] ,
Jianhang Xie ,
Junchao Liang ,
Wei Liu ,
Zhenping Yi ,
Xiaoming Kong  and
Meng Liu
Tianmin Wu
Affiliation:
CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, People’s Republic of China School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China School of Mathematics and Statistics, Shandong University, Weihai, Shandong 264209, People’s Republic of China
Yude Bu*
Affiliation:
School of Mathematics and Statistics, Shandong University, Weihai, Shandong 264209, People’s Republic of China
Jianhang Xie
Affiliation:
School of Mathematics and Statistics, Shandong University, Weihai, Shandong 264209, People’s Republic of China
Junchao Liang
Affiliation:
School of Mathematics and Statistics, Shandong University, Weihai, Shandong 264209, People’s Republic of China
Wei Liu
Affiliation:
School of Mathematics and Statistics, Shandong University, Weihai, Shandong 264209, People’s Republic of China
Zhenping Yi
Affiliation:
School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, Shandong 264209, People’s Republic of China
Xiaoming Kong
Affiliation:
School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, Shandong 264209, People’s Republic of China
Meng Liu
Affiliation:
School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, Shandong 264209, People’s Republic of China
*
Corresponding author: Yude Bu; Email: buyude@sdu.edu.cn
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Abstract

Very metal-poor (VMP, [Fe/H]<-2.0) stars serve as invaluable repositories of insights into the nature and evolution of the first-generation stars formed in the early galaxy. The upcoming China Space Station Telescope (CSST) will provide us with a large amount of spectral data that may contain plenty of VMP stars, and thus it is crucial to determine the stellar atmospheric parameters ($T_{\textrm{eff}}$, $\log$ g, and [Fe/H]) for low-resolution spectra similar to the CSST spectra ($R\sim 200$). This study introduces a novel two-dimensional Convolutional Neural Network (CNN) model, comprised of three convolutional layers and two fully connected layers. The model’s proficiency is assessed in estimating stellar parameters, particularly metallicity, from low-resolution spectra ($R \sim 200$), with a specific focus on enhancing the search for VMP stars within the CSST spectral data. We mainly use 10 008 spectra of VMP stars from LAMOST DR3, and 16 638 spectra of non-VMP stars ([Fe/H]>-2.0) from LAMOST DR8 for the experiments and apply random forest and support vector machine methods to make comparisons. The resolution of all spectra is reduced to $R\sim200$ to match the resolution of the CSST, followed by pre-processing and transformation into two-dimensional spectra for input into the CNN model. The validation and practicality of this model are also tested on the MARCS synthetic spectra. The results show that using the CNN model constructed in this paper, we obtain Mean Absolute Error (MAE) values of 99.40 K for $T_{\textrm{eff}}$, 0.22 dex for $\log$ g, 0.14 dex for [Fe/H], and 0.26 dex for [C/Fe] on the test set. Besides, the CNN model can efficiently identify VMP stars with a precision rate of 94.77%, a recall rate of 93.73%, and an accuracy of 95.70%. This paper powerfully demonstrates the effectiveness of the proposed CNN model in estimating stellar parameters for low-resolution spectra ($R\sim200$) and recognizing VMP stars that are of interest for stellar population and galactic evolution work.

Keywords

Convolutional Neural Networkvery metal-poor starsLAMOSTlow-resolution spectraCSST

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Type
Research Article
Information
Publications of the Astronomical Society of Australia , Volume 41 , 2024 , e002
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© The Author(s), 2023. Published by Cambridge University Press on behalf of the Astronomical Society of Australia

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