Browse by author
Lookup NU author(s): Dr Joachim Harnois-DerapsORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Author(s)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 log10 (|fR0|) = −6 per cent to 2 per cent precision, and log10(H0rc) = 0.5 per cent to 25 per cent precision.
Author(s): Davies CT, Harnois-Deraps J, Li B, Giblin B, Hernandez-Aguayo C, Paillas E
Publication type: Article
Publication status: Published
Journal: Monthly Notices of the Royal Astronomical Society
Year: 2024
Volume: 533
Issue: 3
Pages: 3546-3569
Print publication date: 01/09/2024
Online publication date: 15/08/2024
Acceptance date: 01/08/2024
Date deposited: 17/09/2024
ISSN (print): 0035-8711
ISSN (electronic): 1365-2966
Publisher: Oxford University Press
URL: https://doi.org/10.1093/mnras/stae1966
DOI: 10.1093/mnras/stae1966
Data Access Statement: The MGLenS maps and derived products including data vectors, covariance matrices, and MCMC chains can be made available upon request. The SLICS simulations are publicly available and can be obtained from https://slics.roe.ac.uk
Altmetrics provided by Altmetric
