Our study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approach. Notably, our model makes no prior assumptions about hydrostatic equilibrium. Our results indicate that the model successfully reconstructs unbiased total and gas mass profiles, with a scatter of approximately 10%. We analyse clusters in various dynamical states and mass ranges, finding that our method's accuracy and precision are consistent. We verify the capabilities of our model by comparing it with the hydrostatic equilibrium technique, showing that it accurately recovers total mass profiles without any bias.

A Machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps with THE THREE HUNDRED clusters / Ferragamo, A; De Andres, D; Sbriglio, A; Cui, W; De Petris, M; Yepes, G; Dupuis, R; Jarraya, M; Lahouli, I; De Luca, F; Gianfagna, G; Rasia, E. - In: EPJ WEB OF CONFERENCES. - ISSN 2100-014X. - 293:(2024), pp. 1-6. (Intervento presentato al convegno Observing the Universe at mm wavelenghts, mm Universe 2023 tenutosi a Grenoble, France) [10.1051/epjconf/202429300019].

A Machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps with THE THREE HUNDRED clusters

Ferragamo, A
;
De Petris, M;
2024

Abstract

Our study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approach. Notably, our model makes no prior assumptions about hydrostatic equilibrium. Our results indicate that the model successfully reconstructs unbiased total and gas mass profiles, with a scatter of approximately 10%. We analyse clusters in various dynamical states and mass ranges, finding that our method's accuracy and precision are consistent. We verify the capabilities of our model by comparing it with the hydrostatic equilibrium technique, showing that it accurately recovers total mass profiles without any bias.
2024
Observing the Universe at mm wavelenghts, mm Universe 2023
clusters of galaxies, Sunyaev-Zel’dovich effect; gas mass galaxies
04 Pubblicazione in atti di convegno::04c Atto di convegno in rivista
A Machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps with THE THREE HUNDRED clusters / Ferragamo, A; De Andres, D; Sbriglio, A; Cui, W; De Petris, M; Yepes, G; Dupuis, R; Jarraya, M; Lahouli, I; De Luca, F; Gianfagna, G; Rasia, E. - In: EPJ WEB OF CONFERENCES. - ISSN 2100-014X. - 293:(2024), pp. 1-6. (Intervento presentato al convegno Observing the Universe at mm wavelenghts, mm Universe 2023 tenutosi a Grenoble, France) [10.1051/epjconf/202429300019].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1720355
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