Mass Estimation of Planck Galaxy Clusters using Deep Learning

Abstract

Clusters of galaxies mass can be inferred by indirect observations, see X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical. Unfortunately, all of them are affected by some bias. Alternatively, we provide an independent estimation of the cluster masses from the Planck PLSZ2 catalog 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 physical state are 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.

arXiv commentTo appear in the Proceedings of the International Conference entitled "mm Universe @NIKA2", Rome(Italy), June 2021, EPJ Web of conferences

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Cited by in corpus (8)

• \textsc{The Three Hundred} project: The \textsc{Gizmo-Simba} run
• The CAMELS project: public data release
• Machine Learning and Cosmology
• Self-supervised component separation for the extragalactic submillimeter sky
• kSZ Pairwise Velocity Reconstruction with Machine Learning
• Inferring halo masses with Graph Neural Networks
• Cluster optical depth and pairwise velocity estimation using machine learning
• AutoEnRichness: A hybrid empirical and analytical approach for estimating the richness of galaxy clusters