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

TL;DR

This study refines the selection of QSO candidates in the LMC by integrating multiple diagnostics and machine learning, achieving high confidence with a false positive rate below 1%.

Contribution

It introduces a novel multi-diagnostic approach combined with one-class SVM to improve QSO candidate identification in the LMC.

Findings

Selected 663 high-confidence QSO candidates.

Achieved false positive rate below 1%.

Validated candidates with crossmatching and diagnostics.

Abstract

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%.