Cosmological constraints without nonlinear redshift-space distortions

Nonlinear redshift-space distortions (“fingers of God”) are challenging to model analytically, a fact that limits the applicability of perturbation theory (PT) in redshift space as compared to real space. We show how this problem can be mitigated using a new observable, Q0, which can be easily estimated from the redshift-space clustering data and is approximately equal to the real-space power spectrum. The new statistic does not suffer from fingers of God and can be accurately described with PT down to kmax≃0.4 h Mpc-1. It can be straightforwardly included in the likelihood at negligible additional computational cost and yields noticeable improvements on cosmological parameters compared to standard power spectrum multipole analyses. Using both simulations and observational data from the Baryon Oscillation Spectroscopic Survey, we show that improvements vary from 10% to 100% depending on the cosmological parameter considered, the galaxy sample, and the survey volume. Non-linear redshift-space distortions ("fingers of God") are challenging to model analytically, a fact that limits the applicability of perturbation theory in redshift space as compared to real space. We show how this problem can be mitigated using a new observable, $Q_0$, which can be easily estimated from the redshift space clustering data and is approximately equal to the real space power spectrum. The new statistic does not suffer from fingers of God and can be accurately described with perturbation theory down to $k_{\rm max}\simeq 0.4~h~\text{Mpc}^{-1}$. It can be straightforwardly included in the likelihood at negligible additional computational cost, and yields noticeable improvements on cosmological parameters compared to standard power spectrum multipole analyses. Using both simulations and observational data from the Baryon Oscillation Spectroscopic Survey, we show that improvements vary from $10\%$ to $100\%$ depending on the cosmological parameter considered, the galaxy sample and the survey volume.