The Pan-STARRS1 survey is obtaining multi-epoch imaging in 5 bands ( g _ { P 1 } r _ { P 1 } i _ { P 1 } z _ { P 1 } y _ { P 1 } ) over the entire sky North of declination -30 \deg .
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 , \approx 19 % for stars and \approx 28 % for QSOs .
Moreover , photometric redshifts for 1000 luminous red galaxies up to redshift 0.5 are determined to 2.4 % precision ( defined as 1.48 \times Median|z _ { phot } - z _ { spec } | / ( 1 + z ) ) with just 0.4 % catastrophic outliers and small ( -0.5 % ) residual bias .
For bluer galaxies up to the same redshift the residual bias ( on average -0.5 % ) trend , percentage of catastrophic failures ( 1.2 % ) and precision ( 4.2 % ) are higher , but still interestingly small for many science applications .
Good photometric redshifts ( to 5 % ) can be obtained for at most 60 % of the QSOs of the sample .
PCS will create a value added catalog with classifications and photometric redshifts for eventually many millions sources .