Photometric redshift estimation of galaxies in the P\lowercase{an}-STARRS 3$\ddot{I}€$ survey- I. Methodology

Abstract

We present a photometric redshift (photo-$z$) estimation technique for galaxies in the P\lowercase{an}-STARRS1 (PS1) $3\ddot{I}€$ survey. Specifically, we train and test a regression and a classification Random-Forest (RF) models using photometric features (magnitudes, colors and moments of the radiation intensity) from the optical PS1 data release 2 (PS1-DR2) and from the AllWISE/unWISE infrared source catalogs. The classification RF model ($R{F}_{clas}$) has better performance in the local universe ($z\lesssim 0.1$), while the second one ($R{F}_{reg}$) is on average better for $0.1\lesssim z\lesssim 1$. We adopt as labels the spectroscopic redshift of the galaxies from the Sloan Digital Sky Survey (SDSS) data release 16 (SDSS-DR16). We find that the combination of AllWISE/unWISE and PS1-DR2 features leads to an average bias of $\overline{\hat{I}”z_{norm}}=1\times 10^{-3}$, a standard deviation $\ddot{I}ƒ(\hat{I}”z_{norm})=0.0225$, (where $\hat{I}”z_{norm}\equiv (z_{phot}-z_{spec})/(1+z_{spec})$), and an outlier rate of $P_0=1.48\%$ in the test set for the $R{F}_{clas}$ model. In the low-redshift Universe ($z<0.1$) that is of primary interest to many astronomical transient studies, our model produces an error estimate on the inferred magnitude of an object of $\le$1 mag in 87\% of the test sample.