2011/04/30 by Jessica A. Kirkpatrick, David J. Schlegel, Nicholas P. Ross +6 · 57 citations
Engineering · Physics and Astronomy · #Artificial intelligence #Astronomy #Astronomy and Astrophysical Research #Astrophysics #Baryon #Bayesian probability #Boss #Computer science #Engineering #Galaxies: Formation, Evolution, Phenomena #Galaxy #Gamma-ray bursts and supernovae #Normalization (sociology) #Photometry (optics) #Physics #Probabilistic logic #Quasar #Sky #Stars #astro-ph.CO #astro-ph.IM #physics.data-an
paper · pdf · doi:10.1088/0004-637x/743/2/125
published in The Astrophysical Journal 743(2), 125 (IOP Publishing) · Updated to accepted version for publication in the Astrophysical Journal. 10 pages, 10 figures, 3 tables
arxiv created 2011/09/02 · openalex publication_date 2011/11/29 · arxiv updated 2015/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We present a new method for quasar target selection using photometric fluxes and a Bayesian probabilistic approach. For our purposes, we target quasars using Sloan Digital Sky Survey (SDSS) photometry to a magnitude limit of g = 22. The efficiency and completeness of this technique are measured using the Baryon Oscillation Spectroscopic Survey (BOSS) data taken in 2010. This technique was used for the uniformly selected (CORE) sample of targets in BOSS year-one spectroscopy to be realized in the ninth SDSS data release. When targeting at a density of 40 objects deg −2 (the BOSS quasar targeting density), the efficiency of this technique in recovering z > 2.2 quasars is 40%. The completeness compared to all quasars identified in BOSS data is 65%. This paper also describes possible extensions and improvements for this technique.