  In this work we investigate different machine learning based strategies for
denoising raw simulation data from ProtoDUNE experiment. ProtoDUNE detector is
hosted by CERN and it aims to test and calibrate the technologies for DUNE, a
forthcoming experiment in neutrino physics. Our models leverage deep learning
algorithms to make the first step in the reconstruction workchain, which
consists in converting digital detector signals into physical high level
quantities. We benchmark this approach against traditional algorithms
implemented by the DUNE collaboration. We test the capabilities of graph neural
networks, while exploiting multi-GPU setups to accelerate training and
inference processes.
