Power Spectrum Precision for Redshift Space Distortions

TL;DR

This paper investigates the precision needed in modeling redshift space distortions to accurately measure cosmological parameters, finding that a self-calibrating approach can mitigate biases in future galaxy surveys.

Contribution

It introduces a fitting method using the Kwan-Lewis-Linder reconstruction function that self-calibrates redshift space distortion corrections, reducing bias in cosmological inference.

Findings

Subpercent accuracy is needed for fixed models to avoid bias.

The fitting approach maintains parameter estimation accuracy.

Self-calibration reduces degradation in dark energy and gravity measurements.

Abstract

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.