QSO Candidates in the MACHO LMC database

In these catalogs, we complied number of properties of the objects including RA, Dec, crossmatched IDs with several catalogs, magnitudes, photometric redshifts, etc. See Kim et al. (2011ApJ...735...68K) for the SVM (a.k.a.

Support Vector Machine, a supervised machine learning algorithm) QSO classification model based on the time variability of lightcurves. We used the model to select the 2566 QSO candidates. In this work, we employed multiple diagnostics such as X-ray flux, mid-IR color and AGN SED fitting in order to select 663 promising QSO candidates among the 2,566 candidates.