Clustering Analyses of 300,000 Photometrically Classified Quasars. I. Luminosity and Redshift Evolution in Quasar Bias

By Adam D. Myers, Robert J. Brunner, Robert Nichol, Gordon Richards, D. Schneider, N. Bahcall. 2006

164 citations recorded by the source corpus.

Using ~300,000 photometrically classified quasars, by far the largest quasar sample ever used for such analyses, we study the redshift and luminosity evolution of quasar clustering on scales of ~50 h-1 kpc to ~20 h-1 Mpc from redshifts of z̄ ~ 0.75-2.28. We parameterize our clustering amplitudes using realistic dark matter models and find that a ΛCDM power spectrum provides a superb fit to our data with a redshift-averaged quasar bias of b = 2.41 ± 0.08 (P<χ2 = 0.847) for σ8 = 0.9. This represents a better fit than the best-fit power-law model [ω = (0.0493 ± 0.0064) θ-0.928±0.055; P<χ2 = 0.482]. We find bQ increases with redshift. This evolution is significant at >99.6% using our data set alone, increasing to >99.9999% if stellar contamination is not explicitly parameterized. We measure the quasar classification efficiency across our full sample as a = 95.6 ± %, a star-quasar separation comparable to the star-galaxy separation in many photometric studies of galaxy clustering. We derive the mean mass of the dark matter halos hosting quasars as MDMH = (5.2 ± 0.6) × 1012 h-1 M☉. At z̄ ~ 1.9 we find a 1.5 σ deviation from luminosity-independent quasar clustering; this suggests that increasing our sample size by a factor of ~1.8 could begin to constrain any luminosity dependence in quasar bias at z ~ 2. Our results agree with recent studies of quasar environments at z < 0.4, which detected little luminosity dependence to quasar clustering on proper scales ≳50 h-1 kpc. At z < 1.6, our analysis suggests that bQ is constant with luminosity to within ΔbQ ~ 0.6, and that, for g < 21, angular quasar autocorrelation measurements are unlikely to have sufficient statistical power at z ≲ 1.6 to detect any luminosity dependence in quasars' clustering.

Subfields: Astronomy and Astrophysics, Ecology, Instrumentation

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Citations in the corpus

THE SDSS-IV EXTENDED BARYON OSCILLATION SPECTROSCOPIC SURVEY: OVERVIEW AND EARLY DATA (2015, unknown): “We make BAO projections assuming a flat redshift distribution and the bias relationship b(z) = 0.53 + 0.29(1 + z)2 determined by Croom et al. (2005) and consistent with SDSS measurements (Myers et al. 2007, 2006).”
The clustering of intermediate-redshift quasars as measured by the Baryon Oscillation Spectroscopic Survey (2012, background): “…recently included enough objects to study their clustering with some precision (Porciani, Magliocchetti & Norberg 2004; Croom et al. 2005; Porciani & Norberg 2006; Hennawi et al. 2006a; Myers et al. 2006, 2007a,b; da Angela et al. 2008; Padmanabhan et al. 2009; Ross et al. 2009; Shen et al. 2009).”
The clustering of galaxies in the SDSS-III DR9 Baryon Oscillation Spectroscopic Survey: constraints on primordial non-Gaussianity (2012, methodology): “They are also known to be subject to stellar contamination: Myers et al. (2007) found a stellar contamination of 4.4+1.9−4.4%, for a photometric SDSS data release four (Adelman-McCarthy et al. 2006) quasar sample constructed using the same methodology (Richards et al. 2004) as the quasar samples…”
SELF-CONSISTENT MODELS OF THE AGN AND BLACK HOLE POPULATIONS: DUTY CYCLES, ACCRETION RATES, AND THE MEAN RADIATIVE EFFICIENCY (2007, background): “Points in Figure 14b show recent observational estimates of AGN clustering bias from the 2dF and SDSS quasar redshift surveys (da Angela et al. 2007; Myers et al. 2007; Porciani & Norberg 2007) in the redshift range 0.8 . z. 1.3 and 1.7 . z. 2.1 (Porciani & Norberg 2007) and from SDSS at 3 . z. 3.5…”
The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample: measurement of the growth rate of structure from the anisotropic correlation function between redshift 0.8 and 2.2 (2018, methodology): “Moreover, previous programs such as the 2dF QSO Redshift Survey (2QZ, Croom et al. 2009), SDSS-I/II (Myers et al. 2007; Ross et al. 2009; Shen et al. 2009), SDSS-III/BOSS (White et al. 2012; Eftekharzadeh et al. 2015; Laurent et al. 2016), and a combination of quasar samples from the 2QZ and the 2dF”
THINK OUTSIDE THE COLOR BOX: PROBABILISTIC TARGET SELECTION AND THE SDSS-XDQSO QUASAR TARGETING CATALOG (2010, methodology): “…al. (2004) was used to measure the Integrated Sachs-Wolfe (ISW) effect (Giannantonio et al. 2006, 2008) and cosmic magnification bias (Scranton et al. 2005a), and to study the clustering of quasars on large (Myers et al. 2006, 2007a) and small scales (Hennawi et al. 2006; Myers et al. 2007b, 2008).”
CLUSTERING OF LOW-REDSHIFT (z ⩽ 2.2) QUASARS FROM THE SLOAN DIGITAL SKY SURVEY (2008, methodology): “Myers et al. (2007) estimate errors using an “inverse variance” weighted jackknife technique.”
QUASAR CLUSTERING FROM SDSS DR5: DEPENDENCES ON PHYSICAL PROPERTIES (2008, background): “No strong luminosity dependence has yet been reported (e.g., Adelberger & Steidel 2005; Porciani & Norberg 2006; Myers et al. 2007a; da Ângela et al. 2008); but most of the studies are forz.”
The LABOCA survey of the Extended Chandra Deep Field-South: clustering of submillimetre galaxies (2011, methodology): “We determine babs following the method outlined in section 4.7 of H11, similar to the approach used previously by a number of studies (e.g. Myers et al. 2006, 2007; Coil et al. 2007, 2008, 2009; Hickox et al. 2009); in what follows we briefly describe this procedure.”
Ameliorating systematic uncertainties in the angular clustering of galaxies: a study using the SDSS-III (2011, background): “The jack-knife errors are defined by (see, e.g., Scranton et al. 2002; Myers et al. 2007; Ross et al. 2007) σ2jack(θ) = 19 20 i=20∑ i=1 [w(θ)− wi(θ)]2 , (17) where w(θ) is the measurement over the entire area and wi(θ) is the measurement when the ith jack-knife region is removed.”
A Cosmological Framework for the Co-Evolution of Quasars, Supermassive Black Holes, and Elliptical Galaxies. I. Galaxy Mergers and Quasar Activity (2007, background): “Figure 17 demonstrates that a similar excess is observed in both the quasar-quasar autocorrelation function (Hennawi et al. 2006; Myers et al. 2007b) and the quasar-galaxy cross-correlation function (Serber et al. 2006), arguing that it primarily reflects a genuine preference for quasar activity in…”
Clustering of intermediate redshift quasars using the final SDSS III-BOSS sample (2015, background): “…are Cij = C(ri, rj) = N ∑ L=1 RRL(ri) RR(ri) [ωL(ri)− ω(ri)] × RRL(rj) RR(rj) [ωL(rj)− ω(rj)], (4) where theL subscript refers to the neglected pixel and the random pair ratio,RRL/RR, compensates for the relative area through the relative random catalog size in each pixel (e.g Myers et al. 2007a).”
The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: BAO and RSD measurements from the anisotropic power spectrum of the quasar sample between redshift 0.8 and 2.2 (2020, background): “Due to their high intrinsic luminosity, quasars can be used as tracers of the large scale structure at high redshifts (Croom et al. 2009; Myers et al. 2007; Ross et al. 2009; Shen et al. 2009; White et al. 2012; Karagiannis et al. 2014; Eftekharzadeh et al. 2015; Laurent et al. 2016).”