A Refined QSO Selection Method Using Diagnostics Tests: 663 QSO
  Candidates in the LMC
  We present 663 QSO candidates in the Large Magellanic Cloud (LMC) selected
using multiple diagnostics. We started with a set of 2,566 QSO candidates from
our previous work selected using time variability of the MACHO LMC lightcurves.
We then obtained additional information for the candidates by crossmatching
them with the Spitzer SAGE, the MACHO UBVI, the 2MASS, the Chandra and the XMM
catalogs. Using this information, we specified six diagnostic features based on
mid-IR colors, photometric redshifts using SED template fitting, and X-ray
luminosities in order to further discriminate high confidence QSO candidates in
the absence of spectra information. We then trained a one-class SVM (Support
Vector Machine) model using the diagnostics features of the confirmed 58 MACHO
QSOs. We applied the trained model to the original candidates and finally
selected 663 high confidence QSO candidates. Furthermore, we crossmatched these
663 QSO candidates with the newly confirmed 144 QSOs and 275 non-QSOs in the
LMC fields. On the basis of the counterpart analysis, we found that the false
positive rate is less than 1%.
