Think Outside the Color Box: Probabilistic Target Selection and the SDSS-XDQSO Quasar Targeting Catalog
Jo Bovy (NYU), Joseph F. Hennawi (MPIA), David W. Hogg (NYU, MPIA),, Adam D. Myers (UIUC, MPIA), Jessica A. Kirkpatrick, David J. Schlegel,, Nicholas P. Ross, Erin S. Sheldon, Ian D. McGreer, Donald P. Schneider,, Benjamin A. Weaver

TL;DR
This paper introduces a probabilistic flux-based quasar targeting catalog using the XDQSO method, improving efficiency and accuracy in selecting quasars across various redshifts for SDSS data analysis.
Contribution
The paper develops a novel flux-based quasar selection algorithm using extreme deconvolution, outperforming existing methods especially for medium-redshift quasars, and provides a publicly available code.
Findings
The XDQSO method matches low-redshift quasar selection performance of existing techniques.
It outperforms other flux-based methods for medium-redshift quasar selection.
The catalog covers over 160 million sources in SDSS DR8 imaging.
Abstract
We present the SDSS-XDQSO quasar targeting catalog for efficient flux-based quasar target selection down to the faint limit of the Sloan Digital Sky Survey (SDSS) catalog, even at medium redshifts (2.5 <~ z <~ 3) where the stellar contamination is significant. We build models of the distributions of stars and quasars in flux space down to the flux limit by applying the extreme-deconvolution method to estimate the underlying density. We convolve this density with the flux uncertainties when evaluating the probability that an object is a quasar. This approach results in a targeting algorithm that is more principled, more efficient, and faster than other similar methods. We apply the algorithm to derive low-redshift (z < 2.2), medium-redshift (2.2 <= z <= 3.5), and high-redshift (z > 3.5) quasar probabilities for all 160,904,060 point sources with dereddened i-band magnitude between 17.75…
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