QSO Selection Algorithm Using Time Variability and Machine Learning:
  Selection of 1,620 QSO Candidates from MACHO LMC Database
  We present a new QSO selection algorithm using a Support Vector Machine
(SVM), a supervised classification method, on a set of extracted times series
features including period, amplitude, color, and autocorrelation value. We
train a model that separates QSOs from variable stars, non-variable stars and
microlensing events using 58 known QSOs, 1,629 variable stars and 4,288
non-variables using the MAssive Compact Halo Object (MACHO) database as a
training set. To estimate the efficiency and the accuracy of the model, we
perform a cross-validation test using the training set. The test shows that the
model correctly identifies ~80% of known QSOs with a 25% false positive rate.
The majority of the false positives are Be stars.
  We applied the trained model to 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 and a
few X-ray catalogs. The results further suggest that the majority of the
candidates, more than 70%, are QSOs.
