MGLENS: Modified gravity weak lensing simulations for emulation-based cosmological inference

Joachim Harnois-Déraps*, Cesar Hernandez-Aguayo, Carolina Cuesta-Lazaro, Christian Arnold, Baojiu Li, Christopher T. Davies, Yan Chuan Cai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We present mglens, a large series of modified gravity lensing simulations tailored for cosmic shear data analyses and forecasts in which cosmological and modified gravity parameters are varied simultaneously. Based on the forge and bridgeN-body simulation suites presented in companion papers, we construct 100 × 5000 deg2 of mock Stage-IV lensing data from two 4D Latin hypercubes that sample cosmological and gravitational parameters in f(R) and nDGP gravity, respectively. These are then used to validate our inference analysis pipeline based on the lensing power spectrum, exploiting our implementation of these modified gravity models within the cosmosis cosmological inference package. Sampling this new likelihood, we find that cosmic shear can achieve 95 per cent CL constraints on the modified gravity parameters of log -4.77 and log10[H0rc] > 0.09, after marginalizing over intrinsic alignments of galaxies and including scales up to = 5000. We also investigate the impact of photometric uncertainty, scale cuts, and covariance matrices. We finally explore the consequences of analysing mglens data with the wrong gravity model, and report catastrophic biases for a number of possible scenarios. The Stage-IV mglens simulations, the forge and bridge emulators and the cosmosis interface modules will be made publicly available upon journal acceptance.

Original languageEnglish
Pages (from-to)6336-6358
Number of pages23
JournalMonthly Notices of the Royal Astronomical Society
Issue number4
Early online date7 Sept 2023
Publication statusPublished - 1 Nov 2023

Keywords / Materials (for Non-textual outputs)

  • dark energy
  • dark matter
  • gravitational lensing: Weak
  • large-scale structure of Universe
  • methods: numerical


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