Mass Estimation of Planck Galaxy Clusters using Deep Learning

TL;DR

This paper introduces a CNN-based method to estimate galaxy cluster masses from Planck SZ observations, reducing biases inherent in traditional indirect measurement techniques.

Contribution

It presents a novel machine learning approach trained on hydrodynamic simulations to independently estimate cluster masses without prior assumptions.

Findings

CNN estimates are consistent with Planck masses within bias limits

The method reduces reliance on physical assumptions about cluster symmetry

Results support the presence of a mass bias in Planck measurements

Abstract

Clusters of galaxies mass can be inferred by indirect observations, see X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical. Unfortunately, all of them are affected by some bias. Alternatively, we provide an independent estimation of the cluster masses from the Planck PLSZ2 catalog of galaxy clusters using a machine-learning method. We train a Convolutional Neural Network (CNN) model with the mock SZ observations from The Three Hundred(the300) hydrodynamic simulations to infer the cluster masses from the real maps of the Planck clusters. The advantage of the CNN is that no assumption on a priory symmetry in the cluster's gas distribution or no additional hypothesis about the cluster physical state are made. We compare the cluster masses from the CNN model with those derived by Planck and conclude that the presence of a mass bias is compatible with the simulation results.