A Multi-Wavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates

TL;DR

This paper introduces a machine learning approach that combines X-ray and Sunyaev-Zeldovich data to accurately estimate galaxy cluster mass accretion rates, significantly reducing uncertainty compared to previous methods.

Contribution

The study presents a novel machine learning model trained on simulated data to reliably estimate galaxy cluster mass accretion rates from observational features.

Findings

68% of accretion rates estimated within 33% of true values

Achieved ~58% reduction in scatter over existing methods

Utilizes radial profiles and asymmetries for improved accuracy

Abstract

The mass accretion rate of galaxy clusters is a key factor in determining their structure, but a reliable observational tracer has yet to be established. We present a state-of-the-art machine learning model for constraining the mass accretion rate of galaxy clusters from only X-ray and thermal Sunyaev-Zeldovich observations. Using idealized mock observations of galaxy clusters from the MillenniumTNG simulation, we train a machine learning model to estimate the mass accretion rate. The model constrains 68% of the mass accretion rates of the clusters in our dataset to within 33% of the true value without significant bias, a ~58% reduction in the scatter over existing constraints. We demonstrate that the model uses information from both radial surface brightness density profiles and asymmetries.