Stellar Spectra Classification and Feature evaluation Based on Random Forest. (arXiv:1903.07939v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Li_X/0/1/0/all/0/1">Xiangru Li</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Lin_Y/0/1/0/all/0/1">Yangtao Lin</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Qiu_K/0/1/0/all/0/1">Kaibin Qiu</a>
With the availability of multi-object spectrometers and the designing &
running of some large scale sky surveys, we are obtaining massive spectra.
Therefore, it becomes more and more important to deal with the massive spectral
data efficiently and accurately. This work investigated the classification
problem of stellar spectra under the assumption that there is no perfect
absolute flux calibration, for example, the spectra from Guoshoujing Telescope
(the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST). The
proposed scheme consists of the following two procedures: Firstly, a spectrum
is normalized based on a 17th polynomial fitting; Secondly, a random forest
(RF) is utilized to classifying the stellar spectra. The experiments on four
stellar spectral libraries show that RF has a good classification performance.
This work also studied the spectral feature evaluation problem based on RF. The
evaluation is helpful in understanding the results of the proposed stellar
classification scheme and exploring its potential improvements in future.
With the availability of multi-object spectrometers and the designing &
running of some large scale sky surveys, we are obtaining massive spectra.
Therefore, it becomes more and more important to deal with the massive spectral
data efficiently and accurately. This work investigated the classification
problem of stellar spectra under the assumption that there is no perfect
absolute flux calibration, for example, the spectra from Guoshoujing Telescope
(the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST). The
proposed scheme consists of the following two procedures: Firstly, a spectrum
is normalized based on a 17th polynomial fitting; Secondly, a random forest
(RF) is utilized to classifying the stellar spectra. The experiments on four
stellar spectral libraries show that RF has a good classification performance.
This work also studied the spectral feature evaluation problem based on RF. The
evaluation is helpful in understanding the results of the proposed stellar
classification scheme and exploring its potential improvements in future.
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