A Multiwavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates

Soltis, John; Ntampaka, Michelle; Diemer, Benedikt; ZuHone, John; Bose, Sownak; Delgado, Ana Maria; Hadzhiyska, Boryana; Hernández-Aguayo, César; Nagai, Daisuke; Trac, Hy

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.

Citation

Soltis, J., Ntampaka, M., Diemer, B., ZuHone, J., Bose, S., Delgado, A. M., Hadzhiyska, B., Hernández-Aguayo, C., Nagai, D., & Trac, H. (2025). A Multiwavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates. The Astrophysical Journal, 985(2), Article 212. https://doi.org/10.3847/1538-4357/adcfa4

• Journal Article Type: Article
• Acceptance Date: Apr 21, 2025
• Online Publication Date: May 26, 2025
• Publication Date: Jun 1, 2025
• Deposit Date: Jun 6, 2025
• Publicly Available Date: Jun 6, 2025
• Journal: The Astrophysical Journal
• Electronic ISSN: 1538-4357
• Peer Reviewed: Peer Reviewed
• Volume: 985
• Issue: 2
• Article Number: 212
• DOI: https://doi.org/10.3847/1538-4357/adcfa4
• Keywords: Convolutional neural networks, Neural networks, Galaxy clusters
• Public URL: https://durham-repository.worktribe.com/output/3970927