We present a measurement of the two-point autocorrelation function of photometrically-selected , high- z quasars over \sim 100 deg ^ { 2 } on the Sloan Digitial Sky Survey Stripe 82 field .
Selection is performed using three machine-learning algorithms 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 Spitzer IRAC Equatorial Survey and the Spitzer -HETDEX Exploratory Large-area survey .
Our selection algorithms are trained on the colors of known high- z quasars .
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 \leq z \leq 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 \delta = 1.39 \pm 0.618 and amplitude of \theta _ { 0 } = 0.71 \pm 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 ( \langle z \rangle = 3.38 ) the bias is b = 6.78 \pm 1.79 .
Using this bias , we calculate a characteristic dark-matter halo mass of 1.70–9.83 \times 10 ^ { 12 } h ^ { -1 } M _ { \odot } .
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 .