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.
@article{arxiv.2412.05370,
title = {A Multi-Wavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates},
author = {John Soltis and Michelle Ntampaka and Benedikt Diemer and John ZuHone and Sownak Bose and Ana Maria Delgado and Boryana Hadzhiyska and Cesar Hernandez-Aguayo and Daisuke Nagai and Hy Trac},
journal= {arXiv preprint arXiv:2412.05370},
year = {2024}
}