Recognition of M-type stars in the unclassified spectra of LAMOST DR5 using a hash learning method. (arXiv:1902.03496v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Guo_Y/0/1/0/all/0/1">Y.-X. Guo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Luo_A/0/1/0/all/0/1">A.-L. Luo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Zhang_S/0/1/0/all/0/1">S. Zhang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Du_B/0/1/0/all/0/1">B. Du</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wang_Y/0/1/0/all/0/1">Y.-F. Wang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Chen_J/0/1/0/all/0/1">J.-J. Chen</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Zuo_F/0/1/0/all/0/1">F. Zuo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kong_X/0/1/0/all/0/1">X. Kong</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hou_Y/0/1/0/all/0/1">Y.-H. Hou</a>
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.
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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