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PS1-PSC: Probabilistic Classifications of Unresolved Point Sources in PanSTARRS1

A relatively new source of approach to star-galaxy separation is the classification table derived by Tachibana & Miller (2018, PASP 130, 995).  The authors combined multiple sources of information from the PS1 databases (including the approaches described below) using a sophisticated machine-learning algorithm to estimate the point-source probabilities for 1.5 billion objects.  The resulting table is accessible in MAST Casjobs in the HLSP_PS1_PSC database.  It can be joined with the standard PS1 tables to get accurate classification probabilities for most objects in PS1.  Here is an example Cajobs query that selects objects within 1 arcmin of RA=10 deg, Dec=20 deg:

Code Block
languagesqlthemeConfluence
-- Run this in the PanSTARRS_DR2 Casjobs context

select psc.objid, psc.ps_score, o.nDetections, o.nStackDetections
from fGetNearbyObjEq(10.0, 20.0, 1.0) f
join ObjectThin o on o.objid=f.objid
join HLSP_PS1_PSC.pointsource_scores psc on psc.objid=f.objid

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