Efficient selection of quasar candidates based on optical and infrared photometric data using machine learning.

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 colour spaces, we find that the combination of infrared and optical photometry is more conducive to select quasar candidates. Two new colour criterions (yW1W2 and iW1z W2) are constructed to distinguish quasars from stars efficiently. With iW1zW2, 98.30 per cent of star contamination is eliminated, while 99.50 per cent of quasars are retained, at least to the magnitude limit of our training set of stars. Based on the optical and infrared colour 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 |$99.46{{\ \rm per\ cent}}$| 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 7402 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. [ABSTRACT FROM AUTHOR]