Metallic-Line Stars Identified from Low Resolution Spectra of LAMOST DR5. (arXiv:1904.03242v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+Qin_L/0/1/0/all/0/1">Li Qin</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Luo_A/0/1/0/all/0/1">A-Li Luo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hou_W/0/1/0/all/0/1">Wen Hou</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Li_Y/0/1/0/all/0/1">Yin-Bi Li</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Zhang_S/0/1/0/all/0/1">Shuo Zhang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wang_R/0/1/0/all/0/1">Rui Wang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wang_L/0/1/0/all/0/1">Li-Li Wang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kong_X/0/1/0/all/0/1">Xiao Kong</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Han_J/0/1/0/all/0/1">Jin-Shu Han</a>
LAMOST DR5 released more than 200,000 low resolution spectra of early-type
stars with S/N>50. Searching for metallic-line (Am) stars in such a large
database and study of their statistical properties are presented in this paper.
Six machine learning algorithms were experimented with using known Am spectra,
and both the empirical criteria method(Hou et al. 2015) and the MKCLASS
package(Gray et al. 2016) were also investigated. Comparing their performance,
the random forest (RF) algorithm won, not only because RF has high successful
rate but also it can derives and ranks features. Then the RF was applied to the
early type stars of DR5, and 15,269 Am candidates were picked out. Manual
identification was conducted based on the spectral features derived from the RF
algorithm and verified by experts. After manual identification, 9,372 Am stars
and 1,131 Ap candidates were compiled into a catalog. Statistical studies were
conducted including temperature distribution, space distribution, and infrared
photometry. The spectral types of Am stars are mainly between F0 and A4 with a
peak around A7, which is similar to previous works. With the Gaia distances, we
calculated the vertical height Z from the Galactic plane for each Am star. The
distribution of Z suggests that the incidence rate of Am stars shows a
descending gradient with increasing jZj. On the other hand, Am stars do not
show a noteworthy pattern in the infrared band. As wavelength gets longer, the
infrared excess of Am stars decreases, until little or no excess in W1 and W2
bands.
LAMOST DR5 released more than 200,000 low resolution spectra of early-type
stars with S/N>50. Searching for metallic-line (Am) stars in such a large
database and study of their statistical properties are presented in this paper.
Six machine learning algorithms were experimented with using known Am spectra,
and both the empirical criteria method(Hou et al. 2015) and the MKCLASS
package(Gray et al. 2016) were also investigated. Comparing their performance,
the random forest (RF) algorithm won, not only because RF has high successful
rate but also it can derives and ranks features. Then the RF was applied to the
early type stars of DR5, and 15,269 Am candidates were picked out. Manual
identification was conducted based on the spectral features derived from the RF
algorithm and verified by experts. After manual identification, 9,372 Am stars
and 1,131 Ap candidates were compiled into a catalog. Statistical studies were
conducted including temperature distribution, space distribution, and infrared
photometry. The spectral types of Am stars are mainly between F0 and A4 with a
peak around A7, which is similar to previous works. With the Gaia distances, we
calculated the vertical height Z from the Galactic plane for each Am star. The
distribution of Z suggests that the incidence rate of Am stars shows a
descending gradient with increasing jZj. On the other hand, Am stars do not
show a noteworthy pattern in the infrared band. As wavelength gets longer, the
infrared excess of Am stars decreases, until little or no excess in W1 and W2
bands.
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