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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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