CHEX-MATE: A non-parametric deep learning technique to deproject and deconvolve galaxy cluster X-ray temperature profiles

¹ Université Paris-Saclay, Université Paris-Cité CEA, CNRS, AIM, 91191 Gif-sur-Yvette, France
e-mail: asif.iqbal31@gmail.com
² CEA IRFU/DEDIP, 91191 Gif-sur-Yvette, France
³ INAF – Osservatorio Astronomico di Trieste, Via Tiepolo 11, 34131 Trieste, Italy
⁴ IFPU, Via Beirut 2, 3I-4151 Trieste, Italy
⁵ INAF, Istituto di Astrofisica Spaziale e Fisica Cosmica di Milano, Via A. Corti 12, 20133 Milano, Italy
⁶ Dipartimento di Fisica, Universita’ di Roma “Tor Vergata”, Via Della Ricerca Scientifica, 1, 00133 Roma, Italy
⁷ Dipartimento di Fisica, Sapienza Universitá di Roma, Piazzale Aldo Moro 5, 00185 Roma, Italy
⁸ Department of Physics and Astronomy, Michigan State University, 567 Wilson Rd, East Lansing, MI 48864, USA
⁹ Department of Astronomy, University of Geneva, ch. d’Écogia 16, 1290 Versoix, Switzerland
¹⁰ INAF, Osservatorio di Astrofisica e Scienza dello Spazio, Via Piero Gobetti 93/3, 40129 Bologna, Italy
¹¹ INFN, Sezione di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy
¹² Instituto de Astrofísica de Canarias (IAC), C/ Vía Láctea s/n, 38205 La Laguna, Tenerife, Spain
¹³ Dipartimento di Fisica, Sapienza Universitá di Roma, Piazzale Aldo Moro 5, 00185 Roma, Italy
¹⁴ Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA
¹⁵ Laboratoire d’Astrophysique de Marseille, Aix-Marseille Université, CNRS, CNES, Marseille, France
¹⁶ Institut d’Astrophysique de Paris, CNRS, Sorbonne Université, Paris, France
¹⁷ INAF, Istituto di Astrofisica Spaziale e Fisica Cosmica di Milano, Via A. Corti 12, 20133 Milano, Italy
¹⁸ Center for Astrophysics – Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA
¹⁹ HH Wills Physics Laboratory, University of Bristol, Tyndall Ave, Bristol BS8 1TL, UK
²⁰ IRAP, Université de Toulouse, CNRS, CNES, UT3-UPS, Toulouse, France
²¹ INAF, Osservatorio di Astrofisica e Scienza dello Spazio, Via Piero Gobetti 93/3, 40129 Bologna, Italy
²² INFN, Sezione di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy

Received: 19 June 2023
Accepted: 1 September 2023

Abstract

Temperature profiles of the hot galaxy cluster intracluster medium (ICM) have a complex non-linear structure that traditional parametric modelling may fail to fully approximate. For this study, we made use of neural networks, for the first time, to construct a data-driven non-parametric model of ICM temperature profiles. A new deconvolution algorithm was then introduced to uncover the true (3D) temperature profiles from the observed projected (2D) temperature profiles. An auto-encoder-inspired neural network was first trained by learning a non-linear interpolatory scheme to build the underlying model of 3D temperature profiles in the radial range of [0.02–2] R₅₀₀, using a sparse set of hydrodynamical simulations from the THREE HUNDRED PROJECT. A deconvolution algorithm using a learning-based regularisation scheme was then developed. The model was tested using high and low resolution input temperature profiles, such as those expected from simulations and observations, respectively. We find that the proposed deconvolution and deprojection algorithm is robust with respect to the quality of the data, the morphology of the cluster, and the deprojection scheme used. The algorithm can recover unbiased 3D radial temperature profiles with a precision of around 5% over most of the fitting range. We apply the method to the first sample of temperature profiles obtained with XMM-Newton for the CHEX-MATE project and compared it to parametric deprojection and deconvolution techniques. Our work sets the stage for future studies that focus on the deconvolution of the thermal profiles (temperature, density, pressure) of the ICM and the dark matter profiles in galaxy clusters, using deep learning techniques in conjunction with X-ray, Sunyaev Zel’Dovich (SZ) and optical datasets.