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P1.17: Wang, Rui
Rui Wang (National Astronomical Observatories,Chinese Academy of Sciences, Beijing, China)
Luo, Ali (National Astronomical Observatories,Chinese Academy of Sciences, Beijing,China)






Theme: Machine Learning in Astronomy
Title: Analysis of Stellar Spectra from LAMOST DR5 with Generative Spectrum Networks

We derived the fundamental stellar atmospheric parameters (Teff, log g, [Fe/H] and [α/Fe]) of low-resolution spectroscopy from LAMOST DR5 with Generative Spectrum Networks(GSN), which follows the same scheme as a normal ANN with stellar parameters as the inputs and spectrum as outputs. After training on PHOENIX theoretical spectra, the GSN model performed effectively on producing synthetic spectra. Combining with Bayes framework, application in analysis of LAMOST observed spectra become efficient on the Spark platform. Also, we examined and validated the results by comparing with reference parameters of high-resolution surveys and asteroseismic results. Our method is credible with a precision of ~130K for Teff, ~0.15 dex for log g, ~0.13 dex for [Fe/H] and ~0.10 dex for [α/Fe].

Link to PDF (may not be available yet): P1-17.pdf