We present a new algorithm to estimate quasar photometric redshifts ( photo- z 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- z accuracy , defined as the fraction of quasars with the difference between the photo- z z _ { p } and the spectroscopic redshift z _ { s } , | \Delta z| = |z _ { s } - z _ { p } | / ( 1 + z _ { s } ) within 0.1 , of 74 % .
Combining the WISE W1 and W2 infrared data with the SDSS data , the photo- z accuracy is enhanced to 87 % .
Using the Pan-STARRS1 or DECaLS photometry with WISE W1 and W2 data , the photo- z accuracies are 79 % and 72 % , respectively .
The prior probabilities as a function of magnitude for quasars , stars and galaxies are calculated respectively based on ( 1 ) the quasar luminosity function ; ( 2 ) the Milky Way synthetic simulation with the Besançon model ; ( 3 ) the Bayesian Galaxy Photometric Redshift estimation .
The relative fluxes of stars are obtained with the Padova isochrones , and the relative fluxes of galaxies are modeled through galaxy templates .
We test our classification method to select quasars using the DECaLS g , r , z , and WISE W1 and W2 photometry .
The quasar selection completeness is higher than 70 % for a wide redshift range 0.5 < z < 4.5 , and a wide magnitude range 18 < r < 21.5 mag .
Our photo- z regression and classification method has the potential to extend to future surveys .
The photo- z code will be publicly available .