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26 April 2024 |
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Article overview
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Analysis of Stellar Spectra from LAMOST DR5 with Generative Spectrum Networks | | Wang Rui
; Luo A-li
; Zhang Shuo
; Hou Wen
; Du Bing
; Song Yi-Han
; Wu Ke-Fei
; Chen Jian-Jun
; Zuo Fang
; Qin Li
; Chen Xiang-Lei
; Lu Yan
; | | Date: |
20 Nov 2018 | | Abstract: | In this study, the fundamental stellar atmospheric parameters (Teff, log g,
[Fe/H] and [{alpha}/Fe]) were derived for low-resolution spectroscopy from
LAMOST DR5 with Generative Spectrum Networks (GSN). This follows the same
scheme as a normal artificial neural network with stellar parameters as the
input and spectra as the output. The GSN model was effective in producing
synthetic spectra after training on the PHOENIX theoretical spectra. In
combination with Bayes framework, the application for analysis of LAMOST
observed spectra exhibited improved efficiency on the distributed computing
platform, Spark. In addition, the results were examined and validated by a
comparison with reference parameters from high-resolution surveys and
asteroseismic results. Our results show good consistency with the results from
other survey and catalogs. Our proposed method is reliable with a precision of
80 K for Teff, 0.14 dex for log g, 0.07 dex for [Fe/H] and 0.168 dex for
[{alpha}/Fe], for spectra with a signal-to-noise in g bands (SNRg) higher than
50. The parameters estimated as a part of this work are available at
this http URL | | Source: | arXiv, 1811.8207 | | Services: | Forum | Review | PDF | Favorites | |
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