Constraining modified gravity with weak lensing peaks

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 \, deg², respectively. Finally, we show that peak statistics can constrain log ₁₀(|fR0|) = -6 per cent to 2 per cent precision, and log ₁₀(H₀ rc) = 0.5 per cent to 25 per cent precision.