Mass Estimation of Planck Galaxy Clusters using Deep Learning

Clusters of galaxies mass can be inferred by indirect observations, see X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical. Unfortunately, all them are affected some bias. Alternatively, we provide an independent estimation the cluster masses from Planck PLSZ2 catalog galaxy clusters using a machine-learning method. We train Convolutional Neural Network (CNN) model with mock SZ observations The Three Hundred(the300) hydrodynamic simulations to infer real maps clusters. advantage CNN is that no assumption on priory symmetry in cluster's gas distribution additional hypothesis about physical state made. compare those derived and conclude presence bias compatible simulation results.