Variable Star Identification Using Machine Learning: ROTSEIIId Variable Star Catalogue
Here we present a catalogue of variable stars which are selected, identified and categorised from ROTSE-IIId database of light curves, using machine learning algorithms. The light curves of all the targets from ~1000 pointings observed between 2004-2010 are produce via parallel programming techniques in ~3 months (Güçsav, B.B., et al. 2012). To identify variability and categorise the type of it, initially UPSILoN code (Kim,D.-W., et al. 2015) is used. It was not as successful for our dataset as it was for its original training dataset. Thus, we trained it with known variable stars in the ROTSE-IIId dataset. In paralel, we also tried to write our own code.
