Constraining modified gravity with weak-lensing peaks

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 GR from f(R) and nDGP models at the two-sigma level with a stage-IV survey area of $300 \, \rm {deg}^2$ and $1000 \, \rm {deg}^2$, respectively. Finally, we show that peak statistics can constrain log10(|fR0|) = −6 to 2% precision, and log10(H0rc) = 0.5 to 25% precision.