Redshift space distortions in f(R) gravity
Elise Jennings (1,2), Carlton M. Baugh (3), Baojiu Li (3), Gong-Bo, Zhao (4,5), Kazuya Koyama (4) ((1) KICP, University of Chicago, (2) The, Enrico Fermi Institute, University of Chicago, (3) ICC, Durham University,, (4) ICG, University of Portsmouth, (5) NAOC, Beijing)

TL;DR
This study uses high-precision N-body simulations to analyze redshift space distortions in f(R) gravity, revealing significant deviations from general relativity that could be used to test modified gravity theories with galaxy surveys.
Contribution
First high-resolution nonlinear matter and velocity field simulations in f(R) gravity, demonstrating the potential of velocity power spectrum measurements to constrain deviations from GR.
Findings
Significant deviations in clustering signal between f(R) and GR at z<1.
Velocity power spectrum differs substantially in f(R) models, offering a sensitive probe.
Method to extract matter and velocity power spectra from redshift space data, achieving high accuracy.
Abstract
We use large volume N-body simulations to predict the clustering of dark matter in redshift space in f(R) modified gravity cosmologies. This is the first time that the nonlinear matter and velocity fields have been resolved to such a high level of accuracy over a broad range of scales in this class of models. We find significant deviations from the clustering signal in standard gravity, with an enhanced boost in power on large scales and stronger damping on small scales in the f(R) models compared to GR at redshifts z<1. We measure the velocity divergence (P_\theta \theta) and matter (P_\delta \delta) power spectra and find a large deviation in the ratios \sqrt{P_\theta \theta/P_\delta \delta} and P_\delta \theta/P_\delta\delta, between the f(R) models and GR for 0.03<k/(h/Mpc)<0.5. In linear theory these ratios equal the growth rate of structure on large scales. Our results show that…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
