The SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS), a five-year spectroscopic survey of 10,000 square-degrees, achieved first light in late 2009. One of the key goals of BOSS is to measure the signature of baryon acoustic oscillations (BAO) in the distribution of Ly-α absorption from the spectra of a sample of ~150,000 z > 2.2 quasars in conjunction with measuring the redshifts of 1.6 million luminous red galaxies with high completeness toi ~ 19.9 at z ~ 0.7. One of the biggest challenges in achieving this goal is an efficient target selection algorithm for quasars in the redshift range 2.2 < z < 3.5, where their colors tend to overlap those of the far more numerous stars. During the first year of the BOSS survey, quasar target selection methods were developed and tested to meet the requirement of delivering at least 15 quasars per square-degree in this redshift range, with a goal of 20, out of 40 targets per square-degree allocated to the quasar survey. To achieve these surface densities, the magnitude limit of the quasar targets was set at g ≤ 22.0 or r ≤ 21.85.
In this thesis I present a new method for quasar target selection using photometric fluxes and a Bayesian probabilistic approach. For our purposes I 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 “likelihood” 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 square-degree (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%.
An extension of the “likelihood” technique is also described. This SDSS-XDQSO technique builds 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. I 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.
With BOSS's new catalog of quasar and galaxy data, exciting new science can be done. Whether luminous quasars reside in dark matter halos of the same mass and accrete at different rates, or live in halos of different masses and accretion is near the Eddington limit, is still an open question. Here, I present measurements of the luminosity-dependence of quasar clustering, using QSO data from the Sloan Digital Sky Survey (SDSS) Data Release 7, 2dF-SDSS LRG and QSO Survey (2SLAQ), and SDSS-III: Baryon Oscillation Spectroscopic Survey (BOSS).
In my quasar sample I have 3100 spectroscopically confirmed quasars
with a redshift range of (0.5 < z <1.0), luminosity range of (-27 < M < -21), down to i-band 22.14. In my galaxy sample I have 5.23 million photometric galaxies brighter than z-band = 23.50, selected from the CFHT (Canada-France-Hawaii Telescope) Survey of Stripe-82 (CS82). The cross-correlation is well described by a power law with slope 1.77 ± 0.1 and r0 = 5.05 ± 0.14 h-1 Mpc, which is consistent with previous findings. I determine a large-scale quasar bias, bQSO = 1.46 ± 0.18, at redshift z=0.7. When I divide the quasar sample into low/high luminosity samples I find luminosity depended quasar clustering at a 4.56 σ significance level.