We present a measurement of the two-point autocorrelation function of
photometrically-selected, high-z quasars over ∼ 100 deg2 on the Sloan
Digitial Sky Survey Stripe 82 field. Selection is performed using three
machine-learning algorithms, trained on known high-z 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 2.9≤z≤5.1 and are generally fainter than i=20.2; a regime
which has lacked sufficient number density to perform autocorrelation function
measurements of photometrically-classified quasars. We compute the angular
correlation function of these data, marginally detecting quasar clustering. We
fit a single power-law with an index of δ=1.39±0.618 and amplitude
of θ0=0.71±0.546 arcmin. A dark-matter model is fit to the
angular correlation function to estimate the linear bias. At the average
redshift of our survey (⟨z⟩=3.38) the bias is b=6.78±1.79. Using this bias, we calculate a characteristic dark-matter halo mass of
1.70--9.83×1012h−1M⊙. Our bias estimate suggests that
quasar feedback intermittently shuts down the accretion of gas onto the central
super-massive black hole at early times. If confirmed, these results hint at a
level of luminosity dependence in the clustering of quasars at high-z.Comment: 23 Pages, 17 Figure