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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.
The Pan-STARRS1 survey is collecting multi-epoch, multi-color observations of the sky north of declination -30 deg to unprecedented depths. These data are being photometrically and astrometrically calibrated and will serve as a reference for many oth
The classification of supernovae (SNe) and its impact on our understanding of the explosion physics and progenitors have traditionally been based on the presence or absence of certain spectral features. However, current and upcoming wide-field time-d
We present a robust method to estimate the redshift of galaxies using Pan-STARRS1 photometric data. Our method is an adaptation of the one proposed by Beck et al. (2016) for the SDSS Data Release 12. It uses a training set of 2313724 galaxies for whi
Photometric classification of supernovae (SNe) is imperative as recent and upcoming optical time-domain surveys, such as the Large Synoptic Survey Telescope (LSST), overwhelm the available resources for spectrosopic follow-up. Here we develop a range
Pan-STARRS1 has carried out a set of distinct synoptic imaging sky surveys including the $3pi$ Steradian Survey and the Medium Deep Survey in 5 bands ($grizy_{P1}$). The mean 5$sigma$ point source limiting sensitivities in the stacked 3$pi$ Steradian