Gravitational clustering broadens the count-in-cells distribution of galaxies for surveys along uncorrelated ( well-separated ) lines of sight beyond Poisson noise .
A number of methods have proposed to measure this excess ‘ ‘ cosmic ’ ’ variance to constrain the galaxy bias ( i.e .
the strength of clustering ) independently of the two-point correlation function .
Here we present an observational application of these methods using data from 141 uncorrelated fields ( \sim 700 ~ { } \mathrm { arcmin ^ { 2 } } total ) from Hubble ’ s Brightest of Reionizing Galaxies ( BoRG ) survey .
We use BoRG ’ s broad-band imaging in optical and near infrared to identify N \sim 1000 photometric candidates at z \sim 2 through a combination of colour selection and photometric redshift determination , building a magnitude-limited sample with m _ { AB } \leq 24.5 in F160W .
We detect a clear excess in the variance of the galaxy number counts distribution compared to Poisson expectations , from which we estimate a galaxy bias b \approx 3.63 \pm 0.57 .
When divided by SED-fit classification into \sim 400 early-type and \sim 600 late-type candidates , we estimate biases of b _ { \textit { early } } \approx 4.06 \pm 0.67 and b _ { \textit { late } } \approx 2.98 \pm 0.98 respectively .
These estimates are consistent with previous measurements of the bias from the two-point correlation function , and demonstrate that with N \gtrsim 100 sight-lines , each containing N \gtrsim 5 objects , the counts-in-cell analysis provides a robust measurement of the bias .
This implies that the method can be applied effectively to determine clustering properties ( and characteristic dark-matter halo masses ) of z \sim 6 - 9 galaxies from a pure-parallel James Webb Space Telescope survey similar in design to Hubble ’ s BoRG survey .