Hybrid $P_{\ell}(k)$: general, unified, non-linear matter power spectrum in redshift space

TL;DR

This paper introduces the Hybrid P_ell(k) method, based on the Gaussian Streaming Model, for calculating the non-linear redshift space matter power spectrum multipoles, showing competitive performance against existing models across different gravity theories.

Contribution

It presents a simple, unified approach for the quasi non-linear matter power spectrum in redshift space, tested against simulations in various gravity models, with potential for further refinement.

Findings

The method performs comparably or better than the TNS model for dark matter.

It is competitive for halo multipoles with a linear bias and stochastic term.

The approach shows promise for improved accuracy and unification in the halo model.

Abstract

Constraints on gravity and cosmology will greatly benefit from performing joint clustering and weak lensing analyses on large-scale structure data sets. Utilising non-linear information coming from small physical scales can greatly enhance these constraints. At the heart of these analyses is the matter power spectrum. Here we employ a simple method, dubbed "Hybrid $P_\ell(k)$", based on the Gaussian Streaming Model (GSM), to calculate the quasi non-linear redshift space matter power spectrum multipoles. This employs a fully non-linear and theoretically general prescription for the matter power spectrum. We test this approach against comoving Lagrangian acceleration simulation measurements performed in GR, DGP and $f(R)$ gravity and find that our method performs comparably or better to the dark matter TNS redshift space power spectrum model for dark matter. When comparing the redshift space multipoles for halos, we find that the Gaussian approximation of the GSM with a linear bias and a free stochastic term, $N$, is competitive to the TNS model. Our approach offers many avenues for improvement in accuracy as well as further unification under the halo model.