Subhalo abundance matching in $f(R)$ gravity

TL;DR

This paper predicts galaxy clustering in $f(R)$ gravity using subhalo abundance matching, revealing significant deviations from $ ext{Lambda}$CDM that future surveys can test to constrain gravity models.

Contribution

First application of subhalo abundance matching to $f(R)$ gravity simulations, showing potential for galaxy surveys to test modified gravity theories.

Findings

Galaxy clustering in $f(R)$ models can differ by up to 40% from $ ext{Lambda}$CDM.

Even with similar dark matter clustering, galaxy clustering predictions vary significantly.

Future surveys can use these differences to test the validity of $f(R)$ gravity.

Abstract

Using the liminality N-body simulations of Shi et. al., we present the first predictions for galaxy clustering in $f(R)$ gravity using subhalo abundance matching. We find that, for a given galaxy density, even for an $f(R)$ model with $f_{R0}=-10^{-6}$, for which the cold dark matter clustering is very similar to $\Lambda$CDM, the predicted clustering of galaxies in the $f(R)$ model is very different from $\Lambda$CDM. The deviation can be as large as $40\%$ for samples with mean densities close to that of $L_*$ galaxies. This large deviation is testable given the accuracy that future large-scale galaxy surveys aim to achieve. Our result demonstrates that galaxy surveys can provide a stringent test of General Relativity on cosmological scales, which is comparable to the tests from local astrophysical observations.