The VIMOS Public Extragalactic Redshift Survey (VIPERS): An unbiased
  estimate of the growth rate of structure at $\mathbf{\left<z\right>=0.85}$
  using the clustering of luminous blue galaxies
  We use the VIPERS final data release to investigate the performance of
colour-selected populations of galaxies as tracers of linear large-scale
motions. We empirically select volume-limited samples of blue and red galaxies
as to minimise the systematic error on the estimate of the growth rate
$f\sigma_8$ from the anisotropy of the two-point correlation function. To this
end, rather than rigidly splitting the sample into two colour classes we define
the red/blue fractional contribution of each object through a weight based on
the $(U-V)$ colour distribution. Using mock surveys that are designed to
reproduce the observed properties of VIPERS galaxies, we find the systematic
error in recovering the fiducial value of $f\sigma_8$ to be minimized when
using a volume-limited sample of luminous blue galaxies. We model non-linear
corrections via the Scoccimarro extension of the Kaiser model, finding
systematic errors on $f\sigma_8$ of below $1-2\%$, using scales as small as 5
$h^{-1}\mathrm{Mpc}$. We interpret this result as indicating that selection of
luminous blue galaxies maximises the fraction that are central objects in their
dark matter haloes; this in turn minimises the contribution to the measured
$\xi(r_p,\pi)$ from the 1-halo term, which is dominated by non-linear motions.
The gain is inferior if one uses the full magnitude-limited sample of blue
objects, consistent with the presence of a significant fraction of blue,
fainter satellites dominated by non-streaming, orbital velocities. We measure a
value of $f\sigma_8=0.45 \pm 0.11$ over the single redshift range $0.6\le z\le
1.0$, corresponding to an effective redshift for the blue galaxies
$\left<z\right>=0.85$. Including in the likelihood the potential extra
information contained in the blue-red galaxy cross-correlation function does
not lead to an appreciable improvement in the error bars, while it increases
the systematic error.
