http://arxiv.org/abs/1911.04391
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 to calculate the quasi non-linear redshift space matter power spectrum multipoles from a non-linear prescription for the matter power spectrum, which remains agnostic about gravity or dark energy. 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. When comparing the redshift space multipoles for halos, we find that a linear bias implementation is insufficient to be competitive with the TNS-based model used in the BOSS clustering analysis, which however has additional degrees of freedom. Our approach offers many avenues for improvement in accuracy as well as further unification under the halo model.
B. Bose, A. Pourtsidou, S. Casas, et. al.
Tue, 12 Nov 19
28/84
Comments: 25 pages, 8 figures