BibTeX

@article{2409.20507v1,
Author        = {Moonzarin Reza and Yuanyuan Zhang and Camille Avestruz and Louis E. Strigari and Simone Shevchuk and Francisco Villaescusa-Navarro},
Title         = {Constraining Cosmology with Simulation-based inference and Optical
  Galaxy Cluster Abundance},
Eprint        = {2409.20507v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We test the robustness of simulation-based inference (SBI) in the context of
cosmological parameter estimation from galaxy cluster counts and masses in
simulated optical datasets. We construct ``simulations'' using analytical
models for the galaxy cluster halo mass function (HMF) and for the observed
richness (number of observed member galaxies) to train and test the SBI method.
We compare the SBI parameter posterior samples to those from an MCMC analysis
that uses the same analytical models to construct predictions of the observed
data vector. The two methods exhibit comparable performance, with reliable
constraints derived for the primary cosmological parameters, ($\Omega_m$ and
$\sigma_8$), and richness-mass relation parameters. We also perform
out-of-domain tests with observables constructed from galaxy cluster-sized
halos in the Quijote simulations. Again, the SBI and MCMC results have
comparable posteriors, with similar uncertainties and biases. Unsurprisingly,
upon evaluating the SBI method on thousands of simulated data vectors that span
the parameter space, SBI exhibits worsened posterior calibration metrics in the
out-of-domain application. We note that such calibration tests with MCMC is
less computationally feasible and highlight the potential use of SBI to
stress-test limitations of analytical models, such as in the use for
constructing models for inference with MCMC.},
Year          = {2024},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2409.20507v1},
File          = {2409.20507v1.pdf}
}