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Paper: Automatic QSO Selection Using Machine Learning: Application on Massive Astronomical Database
Volume: 442, Astronomical Data Analysis Software and Systems XX (ADASSXX)
Page: 447
Authors: Kim, D.-W.; Protopapas, P.; Alcock, C.; Byun, Y.-I.; Khardon, R.
Abstract: We present a new QSO (Quasi-Stellar Object) selection algorithm using Support Vector Machine (SVM), a supervised classification method, on a set of multiple extracted times series features such as period, amplitude, color, and autocorrelation value. We train a model that separates QSOs from variable stars, non-variable stars and microlensing events using the richest possible training set consisting of all known types of variables including QSOs from the MAssive Compact Halo Object (MACHO) database. We applied the trained model on the MACHO Large Magellanic Cloud (LMC) dataset, which consists of 40 million lightcurves, and found 1,620 QSO candidates. During the selection none of the 33,242 known MACHO variables were misclassified as QSO candidates. In order to estimate the true false positive rate, we crossmatched the candidates with astronomical catalogs including the Spitzer Surveying the Agents of a Galaxy”s Evolution (SAGE) LMC catalog. The results further suggest that the majority of the candidates, more than 70%, are QSOs.
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