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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 thecolor spaces, we find that the combination of infrared and optical photometryis more conducive to select quasar candidates. Two new color criterions (yW1W2and izW1W2) are constructed to distinguish quasars from stars efficiently. WithizW1W2, 98.30% of star contamination is eliminated, while 99.50% of quasars areretained, at least to the magnitude limit of our training set of stars. Basedon the optical and infrared color features, we put forward an efficient schemato select quasar candidates and high redshift quasar candidates, in which twomachine learning algorithms (XGBoost and SVM) are implemented. The XGBoost andSVM classifiers have proven to be very effective with accuracy of 99.46% when8Color as input pattern and default model parameters. Applying the two optimalclassifiers to the unknown Pan-STARRS and AllWISE cross-matched data set, atotal of 2,006,632 intersected sources are predicted to be quasar candidatesgiven 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 quasarcandidates, a regressor is constructed to estimate their redshifts. Finally7,402 z>3.5 quasars are obtained. Given the magnitude limitation and site ofthe LAMOST telescope, part of these candidates will be used as the inputcatalogue of the LAMOST telescope for follow-up observation, and the rest maybe observed by other telescopes.

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