Estimating Atmospheric Parameters of DA White Dwarf Stars with Deep Learning

Journal Article · Sun Nov 01 04:00:00 EST 2020 · The Astronomical Journal (Online)

DOI:https://doi.org/10.3847/1538-3881/ABBDAA· OSTI ID:23013428

Yang, Yong; Zhao, Jingkun; Zhang, Jiajun; Ye, Xianhao; Zhao, Gang ^[1]

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1. Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101 (China)

The determination of atmospheric parameters of white dwarf stars (WDs) is crucial for researches on them. Traditional methodology is to fit the model spectra to observed absorption lines and report the parameters with the lowest χ² error, which strongly relies on theoretical models that are not always publicly accessible. In this work, we construct a deep learning network to model-independently estimate T_eff and log g of DA stars (DAs), corresponding to WDs with hydrogen-dominated atmospheres. The network is directly trained and tested on the normalized flux pixels of full optical wavelength range of DAs spectroscopically confirmed in the Sloan Digital Sky Survey. Experiments in test yield that the rms error for T_eff and log g approaches 900 K and 0.1 dex, respectively. This technique is applicable for those DAs with T_eff from 5000 to 40,000 K and log g from 7.0 to 9.0 dex. Furthermore, the applicability of this method is verified for the spectra with degraded resolution of ~200. So it is also practical for the analysis of DAs that will be detected by the Chinese Space Station Telescope.

• OSTI ID: 23013428
• Journal Information: The Astronomical Journal (Online), Journal Name: The Astronomical Journal (Online) Journal Issue: 5 Vol. 160; ISSN 1538-3881
• Country of Publication: United States
• Language: English

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
ABSORPTION
HYDROGEN
MACHINE LEARNING
RESOLUTION
SPACE
SPECTRA
STELLAR ATMOSPHERES
TELESCOPES
WAVELENGTHS
WHITE DWARF STARS