Mass Estimation of Planck Galaxy Clusters using Deep Learning

Galaxy cluster masses can be inferred indirectly using measurements from X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical observations. Unfortunately, all of them are affected by some bias. Alternatively, we provide an independent estimation of the cluster masses from the Planck PSZ2 catalogue of galaxy clusters using a machine-learning method. We train a Convolutional Neural Network (CNN) model with the mock SZ observations from The Three Hundred (the300) hydrodynamic simulations to infer the cluster masses from the real maps of the Planck clusters. The advantage of the CNN is that no assumption on a priory symmetry in the cluster’s gas distribution or no additional hypothesis about the cluster’s physical state is made. We compare the cluster masses from the CNN model with those derived by Planck and conclude that the presence of a mass bias is compatible with the simulation results.

Daniel de Andres, Weiguang Cui, Florian Ruppin, Marco De Petris, Gustavo Yepes, Ichraf Lahouli, Gianmarco Aversano, Romain Dupuis, and Mahmoud Jarraya, Mass Estimation of Planck Galaxy Clusters using Deep Learning, EPJ Web of Conferences, January 2022, 257, 00013.

DOI: https://doi.org/10.1051/epjconf/202225700013

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