A Deep Learning Approach to Infer Galaxy Cluster Masses from Planck Compton$-y$ parameter maps

TL;DR

This paper demonstrates that convolutional neural networks trained on hydrodynamic simulations can accurately infer galaxy cluster masses from Planck Compton-y maps, offering an unbiased and independent method that complements traditional approaches.

Contribution

It introduces a CNN-based method trained on simulated data to estimate galaxy cluster masses from Planck y maps, reducing systematic biases inherent in observational assumptions.

Findings

CNN estimates are within 15% bias of Planck measurements.

The approach is unaffected by observational assumptions and biases.

Mass bias explained by hydrostatic equilibrium and scaling law differences.

Abstract

Galaxy clusters are useful laboratories to investigate the evolution of the Universe, and accurately measuring their total masses allows us to constrain important cosmological parameters. However, estimating mass from observations that use different methods and spectral bands introduces various systematic errors. This paper evaluates the use of a Convolutional Neural Network (CNN) to reliably and accurately infer the masses of galaxy clusters from the Compton-y parameter maps provided by the Planck satellite. The CNN is trained with mock images generated from hydrodynamic simulations of galaxy clusters, with Planck's observational limitations taken into account. We observe that the CNN approach is not subject to the usual observational assumptions, and so is not affected by the same biases. By applying the trained CNNs to the real Planck maps, we find cluster masses compatible with Planck measurements within a 15% bias. Finally, we show that this mass bias can be explained by the well known hydrostatic equilibrium assumption in Planck masses, and the different parameters in the Y500-M500 scaling laws. This work highlights that CNNs, supported by hydrodynamic simulations, are a promising and independent tool for estimating cluster masses with high accuracy, which can be extended to other surveys as well as to observations in other bands.