Automatic classification of K2 pulsating stars using machine learning techniques. (arXiv:1906.09611v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+Saux_A/0/1/0/all/0/1">A. Le Saux</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bugnet_L/0/1/0/all/0/1">L. Bugnet</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Mathur_S/0/1/0/all/0/1">S. Mathur</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Breton_S/0/1/0/all/0/1">S. N. Breton</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Garcia_R/0/1/0/all/0/1">R. A. Garcia</a>
The second mission of the NASA Kepler satellite, K2, has collected hundreds
of thousands of lightcurves for stars close to the ecliptic plane. This new
sample could increase the number of known pulsating stars and then improve our
understanding of those stars. For the moment only a few stars have been
properly classified and published. In this work, we present a method to
automaticly classify K2 pulsating stars using a Machine Learning technique
called Random Forest. The objective is to sort out the stars in four classes:
red giant (RG), main-sequence Solar-like stars (SL), classical pulsators (PULS)
and Other. To do this we use the effective temperatures and the luminosities of
the stars as well as the FliPer features, that measures the amount of power
contained in the power spectral density. The classifier now retrieves the right
classification for more than 80% of the stars.
The second mission of the NASA Kepler satellite, K2, has collected hundreds
of thousands of lightcurves for stars close to the ecliptic plane. This new
sample could increase the number of known pulsating stars and then improve our
understanding of those stars. For the moment only a few stars have been
properly classified and published. In this work, we present a method to
automaticly classify K2 pulsating stars using a Machine Learning technique
called Random Forest. The objective is to sort out the stars in four classes:
red giant (RG), main-sequence Solar-like stars (SL), classical pulsators (PULS)
and Other. To do this we use the effective temperatures and the luminosities of
the stars as well as the FliPer features, that measures the amount of power
contained in the power spectral density. The classifier now retrieves the right
classification for more than 80% of the stars.
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