Constraining modified gravity with weak-lensing peaks

Abstract

ABSTRACT It is well established that maximizing the information extracted from upcoming and ongoing stage-IV weak-lensing surveys requires higher order summary statistics that complement the standard two-point statistics. In this work, we focus on weak-lensing peak statistics to test two popular modified gravity models, $f(R)$ and nDGP, using the forge and bridge weak-lensing simulations, respectively. From these simulations, we measure the peak statistics as a function of both cosmological and modified gravity parameters simultaneously. Our findings indicate that the peak abundance is sensitive to the strength of modified gravity, while the peak two-point correlation function is sensitive to the nature of the screening mechanism in a modified gravity model. We combine these simulated statistics with a Gaussian Process Regression emulator and a Gaussian likelihood to generate stage-IV forecast posterior distributions for the modified gravity models. We demonstrate that, assuming small scales can be correctly modelled, peak statistics can be used to distinguish general relativity from $f(R)$ and nDGP models at the 2σ level with a stage-IV survey area of $300$ and $1000 \, \rm {deg}^2$, respectively. Finally, we show that peak statistics can constrain $\log _{10}\left(|f_{R0}|\right) = -6$ per cent to 2 per cent precision, and $\log _{10}(H_0 r_c) = 0.5$ per cent to 25 per cent precision.