  The identification of substructures within halos in cosmological
hydrodynamical simulations is a fundamental step to identify the simulated
counterparts of real objects, namely galaxies. For this reason, substructure
finders play a crucial role in extracting relevant information from the
simulation outputs. They are based on physically-motivated definitions of
substructures, performing multiple steps of particle-by-particle operations,
thus computationally expensive. The purpose of this work is to develop a fast
algorithm to identify substructures in simulations. The final aim, besides a
faster production of subhalo catalogues, is to provide an algorithm fast enough
to be applied with a fine time-cadence during the evolution of the simulations.
We chose to apply the architecture of a well known Fully Convolutional Network,
U-Net, to the identification of substructures within the mass density field of
the simulation. We have developed SubDLe (Substructure identification with Deep
Learning), an algorithm which combines a 3D generalization of U-Net and a
Friends-of-Friends algorithm, and trained it to reproduce the identification of
substructures performed by the SubFind algorithm in a set of zoom-in
cosmological hydrodynamical simulations of galaxy clusters. For the feasibility
study presented in this work, we have trained and tested SubDLe on galaxy
clusters at $z=0$, using a NVIDIA P100 GPU. We focused our tests on the version
of the algorithm working on the identification of purely stellar substructures,
stellar SubDLe. Our stellar SubDLe is capable of identifying the majority of
galaxies in the challenging high-density environment of galaxy clusters in
short computing times. This result has interesting implications in view of the
possibility of integrating fast subhalo finders within simulation codes, that
can take advantage of accelerators available on state-of-art computing nodes.
