THINK OUTSIDE THE COLOR BOX: PROBABILISTIC TARGET SELECTION AND THE SDSS-XDQSO QUASAR TARGETING CATALOG

By Jo Bovy, J. Hennawi, David W. Hogg, Adam D. Myers, J. Kirkpatrick, David Schlegel, N. Ross, Erin Sheldon, I. McGreer, D. Schneider and 1 more. 2010

166 citations recorded by the source corpus.

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 22.45 mag in the 14,555 deg2 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.

Subfields: Artificial Intelligence, Astronomy and Astrophysics

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Citations in the corpus

One-Class Support Measure Machines for Group Anomaly Detection (2013, background): “ty which represents the uncertainty one would obtain from multiple observations is also available. Incorporating these uncertainties has been shown to improve the performance of the learning systems (Bovy et al. 2011, Kirkpatrick et al. 2011, Ross et al. 2012). The anomaly detection has been intensi”
THE SDSS-IV EXTENDED BARYON OSCILLATION SPECTROSCOPIC SURVEY: OVERVIEW AND EARLY DATA (2015, methodology): “As explained in Ross et al. (2012b), the most uniform quasar sample for BOSS was identified from a density of 20 targets deg−2 using the “Extreme Deconvolution” (XDQSO; Bovy et al. 2011a,b) selection.”
THE z = 5 QUASAR LUMINOSITY FUNCTION FROM SDSS STRIPE 82 (2012, methodology): “These probabilities can be used to efficiently select quasar candidates (Bovy et al. 2011; Kirkpatrick et al. 2011).”
The one-dimensional Lyα forest power spectrum from BOSS (2013, methodology): “The quasars, whose spectra cover the Lyα forest of interest for this work, are selected with several algorithms based on the SDSS imaging (Yèche et al. 2010; Kirkpatrick et al. 2011; Bovy et al. 2011; PalanqueDelabrouille et al. 2011), which are all summarized in Ross et al. (2012).”
Active galactic nuclei: what’s in a name? (2017, background): “The density of the largest-area photometric and spectroscopic quasar samples is only ∼150 deg−2, but over a large fraction of the sky, which results in larger samples of AGN overall (e.g. Richards et al. 2009b; D’Abrusco et al. 2009; Bovy et al. 2011; Flesch 2015; Brescia et al. 2015; Pâris et al. 2”
The DESI Experiment Part I: Science,Targeting, and Survey Design (2016, methodology): “In BOSS, the XDQSO algorithm [231] led to nearly 20% improvement compared to color cuts and we can reasonably expect a similar increase in the yield of the QSO selection for DESI by the Random Forest algorithm that we are focusing on.”
THE NINTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY: FIRST SPECTROSCOPIC DATA FROM THE SDSS-III BARYON OSCILLATION SPECTROSCOPIC SURVEY (2012, methodology): “It uses a single selection algorithm, the extreme deconvolution method (hereafter XDQSO) of Bovy et al. (2011), using single-epoch SDSS photometry.”
Clustering of intermediate redshift quasars using the final SDSS III-BOSS sample (2015, methodology): “To circumventthese issues, Bovy et al. (2011) developed theXDQSO algorithm to target quasars forBOSSspectroscopic observation.”
The WISE AGN Catalog (2017, methodology): “Bovy et al. (2011) originally developed and applied an extreme deconvolution technique to build a quasar catalog (XDQSO) from all point sources in Data Release 8 of the Sloan Digital Sky Survey (Aihara et al. 2011).”
Baryon Acoustic Oscillations in the Ly-\alpha\ forest of BOSS quasars (2012, methodology): “The quasar spectroscopy targets are selected from photometric data with a combination of algorithms (Richards et al. 2009; Yeche et al. 2009; Kirkpatrick et al. 2011; Bovy et al. 2011; Palanque-Delabrouille et al. 2011; for a summary, see Ross et al. (2012)).”
The Sloan Digital Sky Survey Quasar Catalog: Fourteenth data release (2017, methodology): “In order to maximize the number of z > 2 quasars, the target selection for SDSS-III/BOSS used a variety of target selection algorithms (Bovy et al. 2011, 2012; Kirkpatrick et al. 2011; Yèche et al. 2010; Palanque-Delabrouille et al. 2011; Richards et al. 2004).”
Column density distribution and cosmological mass density of neutral gas: Sloan Digital Sky Survey-III Data Release 9 (2012, background): “The quasar target selection is described in Ross et al. (2012; see also Bovy et al. 2011).”
The Sloan Digital Sky Survey quasar catalog: tenth data release (2013, methodology): “After testing during the first year of BOSS observations, the CORE selection was chosen to be the XDQSO method (Bovy et al. 2011) The other half of the fibers are dedicated to quasar candidates that are used to maximize the surface density [at the cost of homogeneity] of high-redshift quasars: the…”
The clustering of intermediate-redshift quasars as measured by the Baryon Oscillation Spectroscopic Survey (2012, methodology): “XD is applied in BOSS to model the distributions of quasars and stars in flux space, and hence to separate quasar targets from stellar contaminants (XDQSO; Bovy et al. 2011).”
The Hyper Suprime-Cam Software Pipeline (2017, methodology): “We estimate probability densities for both stars and galaxies using Extreme Deconvolution (XD; Bovy et al. 2011), an algorithm based on mixtures of Gaussians that estimates the probability density from multivariate data, taking into account measurement errors.”
Measurement of BAO correlations at $z=2.3$ with SDSS DR12 \lya-Forests (2017, methodology): “The quasar target selection used in BOSS, summarized in Ross et al. (2012), combines different targeting methods described in Yèche et al. (2010), Kirkpatrick et al. (2011), and Bovy et al. (2011).”
SPECTRAL CLASSIFICATION AND REDSHIFT MEASUREMENT FOR THE SDSS-III BARYON OSCILLATION SPECTROSCOPIC SURVEY (2012, unknown): “The main BOSS survey program consists of two galaxy target samples (Padmanabhan et al. 2012) and a quasar target sample including both color-selected candidates and known quasars (Bovy et al. 2011; Kirkpatrick et al. 2011; Ross et al. 2012b).”
THE ELEVENTH AND TWELFTH DATA RELEASES OF THE SLOAN DIGITAL SKY SURVEY: FINAL DATA FROM SDSS-III (2015, methodology): “The mid-redshift selection includes XDQSOz targets with PQSO > 0.2 that have a pair (from the same mid-z selection) within 1′′ θ 20′′.”