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تسجيل مستخدم جديدPan-STARRS1: Galaxy Clustering in the Small Area Survey 2
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The Pan-STARRS1 survey is currently obtaining imaging in 5 bands (grizy) for the $3pi$ steradian survey, one of the largest optical surveys ever conducted. The finished survey will have spatially varying depth, due to the survey strategy. This paper presents a method to correct galaxy number counts and galaxy clustering for this potential systematic based on a simplified signal to noise measurement. A star and galaxy separation method calibrated using realistic synthetic images is also presented, along with an approach to mask bright stars. By using our techniques on a ~69 sq. degree region of science verification data this paper shows PS1 measurements of the two point angular correlation function as a function of apparent magnitude agree with measurements from deeper, smaller surveys. Clustering measurements appear reliable down to a magnitude limit of rps<22.5. Additionally, stellar contamination and false detection issues are discussed and quantified. This work is the second of two papers which pave the way for the exploitation of the full $3pi$ survey for studies of large scale structure.
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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
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ow 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.
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