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arXiv · 1902.03496

Recognition of M-type stars in the unclassified spectra of LAMOST DR5 using a hash learning method

Y. -X. Guo·A. -L. Luo·S. Zhang·B. Du·Y. -F. Wang·J. -J. Chen·F. Zuo·X. Kong·Y. -H. Hou
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Abstract

Our study aims to recognize M-type stars which are classified as "UNKNOWN" due to bad quality in Large sky Area Multi-Object fibre Spectroscopic Telescope (LAMOST) DR5 V1. A binary nonlinear hashing algorithm based on Multi-Layer Pseudo Inverse Learning (ML-PIL) is proposed to effectively learn spectral features for the M-type star detection, which can overcome the bad fitting problem of template matching, particularly for low S/N spectra. The key steps and the performance of the search scheme are presented. A positive dataset is obtained by clustering the existing M-type spectra to train the ML-PIL networks. By employing this new method, we find 11,410 M-type spectra out of 642,178 "UNKNOWN" spectra, and provide a supplemental catalogue. Both the supplemental objects and released M-type stars in DR5 V1 are composed a whole M type sample, which will be released in the official DR5 to the public in June 2019, All the M-type stars in the dataset are classified to giants and dwarfs by two suggested separators: 1) color diagram of H versus J~K from 2MASS; 2) line indices CaOH versus CaH1, and the separation is validated with HRD derived from Gaia DR2. The magnetic activities and kinematics of M dwarfs are also provided with the EW of H_alpha emission line and the astrometric data from Gaia DR2 respectively.

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Y. -X. Guo, A. -L. Luo, S. Zhang, B. Du, Y. -F. Wang, J. -J. Chen, F. Zuo, X. Kong, Y. -H. Hou. 2019-02-09. Recognition of M-type stars in the unclassified spectra of LAMOST DR5 using a hash learning method. https://doi.org/10.1093/mnras%2Fstz458

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