Efficient Selection of Quasar Candidates Based on Optical and Infrared Photometric Data Using Machine Learning

Abstract

We aim to select quasar candidates based on the two large survey databases, Pan-STARRS and AllWISE. Exploring the distribution of quasars and stars in the color spaces, we find that the combination of infrared and optical photometry is more conducive to select quasar candidates. Two new color criterions (yW1W2 and izW1W2) are constructed to distinguish quasars from stars efficiently. With izW1W2, 98.30% of star contamination is eliminated, while 99.50% of quasars are retained, at least to the magnitude limit of our training set of stars. Based on the optical and infrared color features, we put forward an efficient schema to select quasar candidates and high redshift quasar candidates, in which two machine learning algorithms (XGBoost and SVM) are implemented. The XGBoost and SVM classifiers have proven to be very effective with accuracy of 99.46% when 8Color as input pattern and default model parameters. Applying the two optimal classifiers to the unknown Pan-STARRS and AllWISE cross-matched data set, a total of 2,006,632 intersected sources are predicted to be quasar candidates given quasar probability larger than 0.5 (i.e. P_QSO>0.5). Among them, 1,201,211 have high probability (P_QSO>0.95). For these newly predicted quasar candidates, a regressor is constructed to estimate their redshifts. Finally 7,402 z>3.5 quasars are obtained. Given the magnitude limitation and site of the LAMOST telescope, part of these candidates will be used as the input catalogue of the LAMOST telescope for follow-up observation, and the rest may be observed by other telescopes.