Convolutional neural networks can infer galaxy cluster virial masses and scale radii from 2D projected position and line-of-sight velocity distributions with nearly unbiased results and reduced scatter when richness is added or training is limited to relaxed systems.
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Inferring Halo Mass and Scale Radius of Galaxy Clusters Using Convolutional Neural Networks and Uchuu-UniverseMachine Catalogs
Convolutional neural networks can infer galaxy cluster virial masses and scale radii from 2D projected position and line-of-sight velocity distributions with nearly unbiased results and reduced scatter when richness is added or training is limited to relaxed systems.