A Multiwavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates

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 data set 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.

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Publication Details

Journal
The Astrophysical Journal
Published
2025-05-26
DOI
https://doi.org/10.3847/1538-4357/adcfa4
Citations
2
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
2.31

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article

A Multiwavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates

John A. ZuHone, Hy Trac, Ana Maria Delgado, Sownak Bose et al.
2 citations
The Astrophysical Journal
Galaxies: Formation, Evolution, Phenomena
2.31
article

A Multiwavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates

John A. ZuHone, Hy Trac, Ana Maria Delgado, Sownak Bose, Daisuke Nagai, Benedikt Diemer, John Soltis, César Hernández‐Aguayo, Michelle Ntampaka, Boryana Hadzhiyska
article en
2 citations

Abstract

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 data set 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.

The Astrophysical JournalVol. 985(2)
Space Telescope Science Institute (US), Johns Hopkins University (US), Lawrence Berkeley National Laboratory (US), Durham University (GB), Miller College (US), Yale University (US), Max Planck Institute for Astrophysics (DE), Center for Astrophysics Harvard & Smithsonian (US), University of Maryland, College Park (US), Carnegie Mellon University (US), University of California, Berkeley (US)
National Aeronautics and Space Administration, UK Research and Innovation, Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 12%
Galaxies: Formation, Evolution, Phenomena
2.31
DOIOpen Access PDFOpenAlex
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