We present a photometric redshift (photo-$z$) estimation technique for galaxies in the P\lowercase{ an}-STARRS1 (PS1) $3\pi $ 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 ($RF_{ clas}$) has better performance in the local universe ($z\lesssim 0.1$), while the second one ($RF_{ reg}$) is on average better for $0.1 \lesssim z\lesssim1$. 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 $\overline{ \Delta z_{ norm}}=1\times 10^{ -3}$, a standard deviation $\sigma(\Delta z_{ norm})=0.0225$, (where $\Delta z_{ norm} \equiv (z_{ phot}-z_{ spec})/(1+z_{ spec})$), and an outlier rate of $P_0=1.48 \%$ in the test set for the $RF_{ clas}$ 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.
Categories: astro-ph.GA astro-ph.HE