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A galaxy cluster as the most massive gravitationally-bound object in theUniverse, is dominated by Dark Matter, which unfortunately can only beinvestigated through its interaction with the luminous baryons with somesimplified assumptions that introduce an un-preferred bias. In this work, we,{\it for the first time}, propose a deep learning method based on the U-Netarchitecture, to directly infer the projected total mass density map fromidealised observations of simulated galaxy clusters at multi-wavelengths. Themodel is trained with a large dataset of simulated images from clusters of {\scThe Three Hundred Project}. Although Machine Learning (ML) models do not dependon the assumptions of the dynamics of the intra-cluster medium, our wholemethod relies on the choice of the physics implemented in the hydrodynamicsimulations, which is a limitation of the method. Through different metrics toassess the fidelity of the inferred density map, we show that the predictedtotal mass distribution is in very good agreement with the true simulatedcluster. Therefore, it is not surprising to see the integrated halo mass isalmost unbiased, around 1 per cent for the best result from multiview, and thescatter is also very small, basically within 3 per cent. This result suggeststhat this ML method provides an alternative and more accessible approach toreconstructing the overall matter distribution in galaxy clusters, which cancomplement the lensing method.

The Three Hundred Project: Mapping The Matter Distribution in Galaxy Clusters Via Deep Learning from Multiview Simulated Observations