An improved quasar detection method in EROS-2 and MACHO LMC data sets. http://dx.doi.org/10.1111/j.1365-2966.2012.22061.x

Abstract

We present a new classification method for quasar identification in the EROS-2 and MACHO data sets based on a boosted version of a random forest classifier. We use a set of variability features including parameters of a continuous autoregressive model. We prove that continuous autoregressive parameters are very important discriminators in the classification process. Wecreate two training sets (one for EROS-2 and one for MACHO data sets) using known quasars found in the Large Magellanic Cloud (LMC). Our model’s accuracy in both EROS-2 andMACHO training sets is about 90 per cent precision and 86 per cent recall, improving thestate-of-the-art models, accuracy in quasar detection. We apply the model on the complete, including 28 million objects, EROS-2 and MACHO LMC data sets, finding 1160 and 2551 candidates, respectively. To further validate our list of candidates, we cross-matched our listwith 663 previously known strong candidates, getting 74 percent of matches for MACHO and 40 percent in EROS.The main difference on matching level is because EROS-2 is a slightly shallower survey which translates to significantly lower signal-to-noise ratio light curves.