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In this study, the fundamental stellar atmospheric parameters (Teff, log g,[Fe/H] and [{\alpha}/Fe]) were derived for low-resolution spectroscopy fromLAMOST DR5 with Generative Spectrum Networks (GSN). This follows the samescheme as a normal artificial neural network with stellar parameters as theinput and spectra as the output. The GSN model was effective in producingsynthetic spectra after training on the PHOENIX theoretical spectra. Incombination with Bayes framework, the application for analysis of LAMOSTobserved spectra exhibited improved efficiency on the distributed computingplatform, Spark. In addition, the results were examined and validated by acomparison with reference parameters from high-resolution surveys andasteroseismic results. Our results show good consistency with the results fromother survey and catalogs. Our proposed method is reliable with a precision of80 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 than50. The parameters estimated as a part of this work are available athttp://paperdata.china-vo.org/GSN_parameters/GSN_parameters.csv.

Analysis of Stellar Spectra from LAMOST DR5 with Generative Spectrum Networks