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 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.
