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We present 663 QSO candidates in the Large Magellanic Cloud (LMC) selectedusing multiple diagnostics. We started with a set of 2,566 QSO candidates fromour previous work selected using time variability of the MACHO LMC lightcurves.We then obtained additional information for the candidates by crossmatchingthem with the Spitzer SAGE, the MACHO UBVI, the 2MASS, the Chandra and the XMMcatalogs. Using this information, we specified six diagnostic features based onmid-IR colors, photometric redshifts using SED template fitting, and X-rayluminosities in order to further discriminate high confidence QSO candidates inthe absence of spectra information. We then trained a one-class SVM (SupportVector Machine) model using the diagnostics features of the confirmed 58 MACHOQSOs. We applied the trained model to the original candidates and finallyselected 663 high confidence QSO candidates. Furthermore, we crossmatched these663 QSO candidates with the newly confirmed 144 QSOs and 275 non-QSOs in theLMC fields. On the basis of the counterpart analysis, we found that the falsepositive rate is less than 1%.

A Refined QSO Selection Method Using Diagnostics Tests: 663 QSO Candidates in the LMC