BibTeX

@article{2410.10616v1,
Author        = {A. Spurio Mancini and K. Lin and J. D. McEwen},
Title         = {Field-level cosmological model selection: field-level simulation-based
  inference for Stage IV cosmic shear can distinguish dynamical dark energy},
Eprint        = {2410.10616v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We present a framework that for the first time allows Bayesian model
comparison to be performed for field-level inference of cosmological models. We
achieve this by taking a simulation-based inference (SBI) approach using neural
likelihood estimation, which we couple with the learned harmonic mean estimator
in order to compute the Bayesian evidence for model comparison. We apply our
framework to mock Stage IV cosmic shear observations to assess its
effectiveness at distinguishing between various models of dark energy. If the
recent DESI results that provided exciting hints of dynamical dark energy were
indeed the true underlying model, our analysis shows Stage IV cosmic shear
surveys could definitively detect dynamical dark energy. We also perform
traditional power spectrum likelihood-based inference for comparison, which we
find is not able to distinguish between dark energy models, highlighting the
enhanced constraining power for model comparison of our field-level SBI
approach.},
Year          = {2024},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2410.10616v1},
File          = {2410.10616v1.pdf}
}