Cosmology with galaxy clusters using machine learning. Application to eROSITA Data

TL;DR

This paper demonstrates that machine learning trained on hydrodynamical simulations can infer cosmological parameters from galaxy cluster X-ray data, providing results comparable to traditional methods and highlighting the potential of large-scale surveys.

Contribution

It introduces a novel ML-based approach for cosmological inference from galaxy clusters using multi-cosmology simulations, bypassing traditional mass calibration issues.

Findings

Achieved cosmological constraints comparable to standard analyses.

Found no tension in $\

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Abstract

Context: We present the first Cosmological Parameter inferences from eROSITA X-ray observations of galaxy clusters using a Machine Learning algorithm. Methods: We train a Random Forest using mock catalogs of clusters from Magneticum multi-cosmology hydrodynamical simulations. We apply the trained ML algorithm to observed X-ray features (gas luminosity, mass, and temperature) at different redshifts from the eROSITA eFEDS and eRASS1 catalogs. Results: We obtain cosmological constraints with precision comparable to those from standard analyses, such as weak lensing and cluster abundances. We infer $\Omega_{\rm m}=0.30^{+0.03}_{-0.02}$, $\sigma_8=0.81\pm0.01$, and $h_0=0.710\pm0.004$. The recovered parameters show no tension in the $\Omega_{\rm m}-\sigma_8$ space, but a significant deviation of $h_0$ from the Planck estimates. These inferences remain rather stable against variations of the input observable set and parameter space coverage. These results indicate that correlations among intracluster properties contain cosmological information beyond that encoded in the cluster abundance alone, which can be captured by machine learning trained on multi-cosmology simulations. Conclusions: ML algorithms trained on multi-cosmology hydrodynamical simulations can effectively infer cosmological parameters directly from galaxy cluster data. This is a change of paradigm in the context of cosmological parameter inferences. This approach complements traditional cluster-count analyses and is particularly suited to large upcoming surveys, where systematic uncertainties in mass calibration may otherwise dominate the error budget. It also highlights the potential of large-scale X-ray surveys to deliver independent tests of the standard cosmological model.