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Galaxy cluster count cosmology with simulation-based inference

M Regamey, D Eckert, R Seppi, W Hartley… - arXiv preprint arXiv …, 2025 - arxiv.org
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… the observed cluster population with simulation-based inference. We construct a simulation … We conclude that simulation-based inference is a promising avenue for future …

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@article{2506.05457v1,
Author        = {M. Regamey and D. Eckert and R. Seppi and W. Hartley and K. Umetsu and S. Tam and D. Gerolymatou},
Title         = {Galaxy cluster count cosmology with simulation-based inference},
Eprint        = {2506.05457v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {The abundance and mass distribution of galaxy clusters is a sensitive probe
of cosmological parameters, through the sensitivity of the high-mass end of the
halo mass function to $\Omega_m$ and $\sigma_8$. While galaxy cluster surveys
have been used as cosmological probes for more than a decade, the accuracy of
cluster count experiments is still hampered by systematic, such as the relation
between observables and halo mass, the accuracy of the halo mass function, and
the survey selection function. Here we show that these uncertainties can be
alleviated by forward modeling the observed cluster population with
simulation-based inference. We construct a pipeline that predicts the
distribution of observables from cosmological parameters and scaling relations,
and then train a neural network to learn the mapping between the input
parameters and the measured distributions. We focus on fiducial X-ray surveys
with available flux, temperature, and redshift measurements, although the
method can be easily adapted to any available observable. We apply our method
to mock samples extracted from the UNIT1i simulation and demonstrate the
accuracy of our approach. We then study the impact of several systematic
uncertainties on the recovered cosmological parameters. We show that sample
variance and the choice of the halo mass function are subdominant sources of
uncertainty. Conversely, the absolute mass scale is the leading source of
systematic error and must be calibrated at the $<10\%$ level to recover
accurate values of $\Omega_m$ and $\sigma_8$. However, the quantity
$S_8=\sigma_8(\Omega_m/0.3)^{0.3}$ appears to be less sensitive to the accuracy
of the mass calibration. We conclude that simulation-based inference is a
promising avenue for future cosmological studies from galaxy cluster surveys
such as eROSITA and Euclid as it allows to consider all the available
observables in a straightforward manner.},
Year          = {2025},
Month         = {Jun},
Url           = {http://arxiv.org/abs/2506.05457v1},
File          = {2506.05457v1.pdf}
}

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