  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.
