Quasar Photometric Redshifts and Candidate Selection: A New Algorithm Based on Optical and Mid-Infrared Photometric Data
Qian Yang, Xue-Bing Wu, Xiaohui Fan, Linhua Jiang, Ian McGreer,, Richard Green, Jinyi Yang, Jan-Torge Schindler, Feige Wang, Wenwen Zuo,, Yuming Fu

TL;DR
This paper introduces a novel algorithm for estimating quasar photometric redshifts using optical and infrared data, achieving high accuracy and effective quasar candidate selection across various surveys.
Contribution
The new algorithm models relative flux distributions with multivariate Skew-t distributions and incorporates prior probabilities, improving photo-$z$ accuracy and quasar classification over existing methods.
Findings
Photo-$z$ accuracy of 74% with SDSS data
Accuracy improves to 87% with combined SDSS and WISE data
Quasar selection completeness exceeds 70% across broad redshift and magnitude ranges
Abstract
We present a new algorithm to estimate quasar photometric redshifts (photo-s), by considering the asymmetries in the relative flux distributions of quasars. The relative flux models are built with multivariate Skew-t distributions in the multi-dimensional space of relative fluxes as a function of redshift and magnitude. For 151,392 quasars in the SDSS, we achieve a photo- accuracy, defined as the fraction of quasars with the difference between the photo- and the spectroscopic redshift , within 0.1, of 74%. Combining the WISE W1 and W2 infrared data with the SDSS data, the photo- accuracy is enhanced to 87%. Using the Pan-STARRS1 or DECaLS photometry with WISE W1 and W2 data, the photo- accuracies are 79% and 72%, respectively. The prior probabilities as a function of magnitude for quasars, stars and galaxies are calculated…
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