Think outside the color box: Probabilistic target selection and the SDSS-XDQSO quasar targeting catalog

Jo Bovy, Joseph F. Hennawi, David W. Hogg, Adam D. Myers, Jessica A. Kirkpatrick, David J. Schlegel, Nicholas P. Ross, Erin S. Sheldon, Ian D. McGreer, Donald P. Schneider, Benjamin A. Weaver

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152 Scopus citations

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 and 22.45mag in the 14,555deg2 of imaging from SDSS Data Release 8. The catalog can be used to define a uniformly selected and efficient low- or medium-redshift quasar survey, such as that needed for the SDSS-III's Baryon Oscillation Spectroscopic Survey project. We show that the XDQSO technique performs as well as the current best photometric quasar-selection technique at low redshift, and outperforms all other flux-based methods for selecting the medium-redshift quasars of our primary interest. We make code to reproduce the XDQSO quasar target selection publicly available.

Original languageEnglish (US)
Article number141
JournalAstrophysical Journal
Volume729
Issue number2
DOIs
StatePublished - Mar 10 2011

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

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