  Galaxy clusters are useful laboratories to investigate the evolution of the
Universe, and accurately measuring their total masses allows us to constrain
important cosmological parameters. However, estimating mass from observations
that use different methods and spectral bands introduces various systematic
errors. This paper evaluates the use of a Convolutional Neural Network (CNN) to
reliably and accurately infer the masses of galaxy clusters from the Compton-y
parameter maps provided by the Planck satellite. The CNN is trained with mock
images generated from hydrodynamic simulations of galaxy clusters, with
Planck's observational limitations taken into account. We observe that the CNN
approach is not subject to the usual observational assumptions, and so is not
affected by the same biases. By applying the trained CNNs to the real Planck
maps, we find cluster masses compatible with Planck measurements within a 15%
bias. Finally, we show that this mass bias can be explained by the well known
hydrostatic equilibrium assumption in Planck masses, and the different
parameters in the Y500-M500 scaling laws. This work highlights that CNNs,
supported by hydrodynamic simulations, are a promising and independent tool for
estimating cluster masses with high accuracy, which can be extended to other
surveys as well as to observations in other bands.
