The Three Hundred project: A Machine Learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps

TL;DR

This paper presents a machine learning approach combining an autoencoder and random forest to accurately infer 3D galaxy cluster mass profiles from SZ maps without assuming hydrostatic equilibrium, achieving about 10% bias.

Contribution

The novel method infers unbiased 3D mass profiles from SZ maps using a combined autoencoder and random forest, independent of hydrostatic equilibrium assumptions.

Findings

Profiles are unbiased with ~10% scatter.

Method works across various cluster dynamical states.

Reproduces trustworthy mass-concentration relation.

Abstract

We develop a machine learning algorithm to infer the 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich effect maps. We generate around 73,000 mock images along various lines of sight using 2,522 simulated clusters from the \thethreehundred{} project at redshift $z< 0.12$ and train a model that combines an autoencoder and a random forest. Without making any prior assumptions about the hydrostatic equilibrium of the clusters, the model is capable of reconstructing the total mass profile as well as the gas mass profile, which is responsible for the SZ effect. We show that the recovered profiles are unbiased with a scatter of about $10\%$, slightly increasing towards the core and the outskirts of the cluster. We selected clusters in the mass range of $10^{13.5} \leq M_{200} /(\hMsun) \leq 10^{15.5}$, spanning different dynamical states, from relaxed to disturbed halos. We verify that both the accuracy and precision of this method show a slight dependence on the dynamical state, but not on the cluster mass. To further verify the consistency of our model, we fit the inferred total mass profiles with an NFW model and contrast the concentration values with those of the true profiles. We note that the inferred profiles are unbiased for higher concentration values, reproducing a trustworthy mass-concentration relation. The comparison with a widely used mass estimation technique, such as hydrostatic equilibrium, demonstrates that our method recovers the total mass that is not biased by non-thermal motions of the gas.