Clustering of intermediate redshift quasars using the final SDSS III-BOSS sample

TL;DR

This study measures the clustering of intermediate redshift quasars using the SDSS BOSS sample, providing the most precise bias measurement at these redshifts and insights into quasar host halo properties and evolution.

Contribution

It offers the most precise quasar bias measurement at z ~ 2.5 using a large, uniform sample, and investigates clustering evolution and luminosity dependence.

Findings

Quasar bias at z ~ 2.5 is 3.54 +/- 0.10.

Correlation length r0 is approximately 8.12 Mpc/h.

No significant evolution of clustering over 2.2 < z < 3.4.

Abstract

We measure the two-point clustering of spectroscopically confirmed quasars from the final sample of the Baryon Oscillation Spectroscopic Survey (BOSS) on comoving scales of 4 < s < 22 Mpc/h. The sample covers 6950 deg^2 (~ 19 (Gpc/h)^3) and, over the redshift range 2.2 < z < 2.8, contains 55,826 homogeneously selected quasars, which is twice as many as in any similar work. We deduce b_Q = 3.54 +/- 0.10 ; the most precise measurement of quasar bias to date at these redshifts. This corresponds to a host halo mass of ~ 2 x 10^12 ~ M_sun/h with an implied quasar duty cycle of ~1 percent. The real-space projected correlation function is well-fit by a power law of index -2 and correlation length r0 = (8.12 +/- 0.22), Mpc/h over scales of 4 < rp < 25 ~ Mpc/h. To better study the evolution of quasar clustering at moderate redshift, we extend the redshift range of our study to z ~ 3.4 and measure the bias and correlation length of three subsamples over 2.2 < z < 3.4. We find no significant evolution of r0 or bias over this range, implying that the host halo mass of quasars decreases somewhat with increasing redshift. We find quasar clustering remains similar over a decade in luminosity, contradicting a scenario in which quasar luminosity is monotonically related to halo mass at z ~ 2.5. Our results are broadly consistent with previous BOSS measurements, but they yield more precise constraints based upon a larger and more uniform data set.