Refined Classification

In the second work of QSO classification (Kim+ 2012), we selected 663 high confidence QSO candidates in the Large Magellanic Cloud (LMC) selected using multiple diagnostics. We started with a set of 2,566 QSO candidates selected using the methodology presented in our previous work (Kim+ 2011) based on variability of the MACHO LMC light curves. We then obtained additional information for the candidates by crossmatching them with Spitzer SAGE, 2MASS, Chandra, XMM, and an LMC UBVI catalog. 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. Figure 1 shows these diagnostics.

[ Figure 1. Illustration of the processes for selecting high confidence QSO candidates ]

We then trained a one-class Support Vector Machine (SVM) 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 151 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%. Figure 2 shows light curves of high confidence QSOs.

[ Figure 2. High confidence QSO light curves ]