Power Spectrum Precision for Redshift Space Distortions
  Redshift space distortions in galaxy clustering offer a promising technique
for probing the growth rate of structure and testing dark energy properties and
gravity. We consider the issue of to what accuracy they need to be modeled in
order not to unduly bias cosmological conclusions. Fitting for nonlinear and
redshift space corrections to the linear theory real space density power
spectrum in bins in wavemode, we analyze both the effect of marginalizing over
these corrections and of the bias due to not correcting them fully. While
naively subpercent accuracy is required to avoid bias in the fixed case, in the
fitting approach the Kwan-Lewis-Linder reconstruction function for redshift
space distortions is found to be accurately selfcalibrated with little
degradation in dark energy and gravity parameter estimation for a next
generation galaxy redshift survey such as BigBOSS.
