Cosmology with Galaxy Cluster Properties using Machine Learning
Abstract
[Abridged] Galaxy clusters are the most massive gravitationallybound systems in the universe and are widely considered to be an effective cosmological probe. We propose the first Machine Learning method using galaxy cluster properties to derive unbiased constraints on a set of cosmological parameters, including Omega_m, sigma_8, Omega_b, and h_0. We train the machine learning model with mock catalogs including "measured" quantities from Magneticum multicosmology hydrodynamical simulations, like gas mass, gas bolometric luminosity, gas temperature, stellar mass, cluster radius, total mass, velocity dispersion, and redshift, and correctly predict all parameters with uncertainties of the order of ~14% for Omega_m, ~8% for sigma_8, ~6% for Omega_b, and ~3% for h_0. This first test is exceptionally promising, as it shows that machine learning can efficiently map the correlations in the multidimensional space of the observed quantities to the cosmological parameter space and narrow down the probability that a given sample belongs to a given cosmological parameter combination. In the future, these ML tools can be applied to cluster samples with multiwavelength observations from surveys like LSST, CSST, Euclid, Roman in optical and nearinfrared bands, and eROSITA in Xrays, to constrain both the cosmology and the effect of the baryonic feedback.
 Publication:

arXiv eprints
 Pub Date:
 April 2023
 DOI:
 10.48550/arXiv.2304.09142
 arXiv:
 arXiv:2304.09142
 Bibcode:
 2023arXiv230409142Q
 Keywords:

 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Astrophysics  Instrumentation and Methods for Astrophysics
 EPrint:
 19 pages, 20 figures. Revised version after the referee report. Resubmitted to A&