The Photometric Classification Server for Pan-STARRS1

TL;DR

The paper describes the implementation and testing of the Photometric Classification Server for Pan-STARRS1, enabling automated classification and redshift estimation of celestial objects using multi-band photometry, with performance comparable to SDSS.

Contribution

It introduces a new automated system for classifying objects and estimating redshifts in Pan-STARRS1 data, demonstrating comparable accuracy to SDSS and providing a scalable catalog solution.

Findings

Stars classified with 85% accuracy

Galaxies classified with 97% accuracy

Photometric redshifts for luminous red galaxies with 2.4% precision

Abstract

The Pan-STARRS1 survey is obtaining multi-epoch imaging in 5 bands (gps rps ips zps yps) over the entire sky North of declination -30deg. We describe here the implementation of the Photometric Classification Server (PCS) for Pan-STARRS1. PCS will allow the automatic classification of objects into star/galaxy/quasar classes based on colors, the measurement of photometric redshifts for extragalactic objects, and constrain stellar parameters for stellar objects, working at the catalog level. We present tests of the system based on high signal-to-noise photometry derived from the Medium Deep Fields of Pan-STARRS1, using available spectroscopic surveys as training and/or verification sets. We show that the Pan-STARRS1 photometry delivers classifications and photometric redshifts as good as the Sloan Digital Sky Survey (SDSS) photometry to the same magnitude limits. In particular, our preliminary results, based on this relatively limited dataset down to the SDSS spectroscopic limits and therefore potentially improvable, show that stars are correctly classified as such in 85% of cases, galaxies in 97% and QSOs in 84%. False positives are less than 1% for galaxies, ~19% for stars and ~28% QSOs. Moreover, photometric redshifts for 1000 luminous red galaxies up to redshift 0.5 are determined to 2.4% precision with just 0.4% catastrophic outliers and small (-0.5%) residual bias. PCS will create a value added catalog with classifications and photometric redshifts for eventually many millions sources.