A Simple Likelihood Method for Quasar Target Selection

TL;DR

This paper introduces a Bayesian probabilistic method for selecting quasars from SDSS photometry, achieving 40% efficiency and 65% completeness for z>2.2 quasars at typical survey densities, with potential for further improvements.

Contribution

The paper presents a novel Bayesian likelihood approach for quasar target selection using photometric data, demonstrating its effectiveness with SDSS and BOSS data.

Findings

Achieves 40% efficiency in recovering z>2.2 quasars.

Attains 65% completeness compared to all quasars in BOSS data.

Method is applicable for large-scale quasar surveys.

Abstract

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 is 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 9th SDSS data release. When targeting at a density of 40 objects per sq-deg (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