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

Understand

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.