The Clustering of High-Redshift (2.9 $\leq$ z $\leq$ 5.1) Quasars in SDSS Stripe 82
John D. Timlin (1), Nicholas P. Ross (2), Gordon T. Richards (1), Adam, D. Myers (3), Andrew Pellegrino (1), Franz E. Bauer (4,5,6), Mark Lacy (7),, Donald P. Schneider (8,9), Edward J. Wollack (10), Nadia L. Zakamska (11), ((1) Drexel University, (2) Institute for Astronomy

TL;DR
This study measures the clustering of high-redshift quasars using machine learning-selected samples in Stripe 82, revealing their bias and dark matter halo masses, and suggesting early black hole growth regulation.
Contribution
First measurement of high-$z$ quasar clustering using photometric selection with machine learning in Stripe 82, providing insights into their bias and host halo masses.
Findings
Detected quasar clustering with a power-law index of 1.39
Estimated quasar bias at $z \,=\, 3.38$ as 6.78
Derived dark matter halo masses between 1.70 and 9.83×10^{12} h^{-1} M_{\
Abstract
We present a measurement of the two-point autocorrelation function of photometrically-selected, high- quasars over 100 deg on the Sloan Digitial Sky Survey Stripe 82 field. Selection is performed using three machine-learning algorithms, trained on known high- quasar colors, in a six-dimensional, optical/mid-infrared color space. Optical data from the Sloan Digitial Sky Survey is combined with overlapping deep mid-infrared data from the \emph{Spitzer} IRAC Equatorial Survey and the \emph{Spitzer}-HETDEX Exploratory Large-area survey. The selected quasar sample consists of 1378 objects and contains both spectroscopically-confirmed quasars and photometrically-selected quasar candidates. These objects span a redshift range of and are generally fainter than ; a regime which has lacked sufficient number density to perform autocorrelation function…
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