A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel’dovich maps with The Three Hundred clusters

¹ Instituto de Astrofísica de Canarias (IAC), C/ Vía Láctea s/n, E-38205 La Laguna, Tenerife, Spain
² Universidad de La Laguna, Departamento de Astrofísica, C/ Astrofísico Francisco Sánchez s/n, E-38206 La Laguna, Tenerife, Spain
³ Dipartimento di Fisica, Sapienza Universitá di Roma, Piazzale Aldo Moro 5, I-00185 Roma, Italy
⁴ Departamento de Física Téorica, Módulo 15, Facultad de Ciencias, Universidad Autónoma de Madrid, E-28049 Madrid, Spain
⁵ Centro de Investigación Avanzada en Física Fundamental (CIAFF), Facultad de Ciencias, Universidad Autónoma de Madrid, E-28049 Madrid, Spain
⁶ Institute for Astronomy, University of Edinburgh, Edinburgh EH9 3HJ, United Kingdom
⁷ EURANOVA, Mont-Saint-Guibert, Belgium
⁸ Dipartimento di Fisica, Università di Roma Tor Vergata, Via della Ricerca Scientifica 1, I-00133 Roma, Italy
⁹ INAF - Istituto di Astrofisica e Planetologia Spaziali, via Fosso del Cavaliere 100, I-00133 Roma, Italy
¹⁰ IFPU - Institute for Fundamental Physics of the Universe, Via Beirut 2, I-34014 Trieste, Italy
¹¹ INAF Osservatorio Astronomico di Trieste, via Tiepolo 11, I-34131, Trieste, Italy

^* e-mail: antonio.ferragamo@uniroma1.it

Published online: 28 March 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.