Deep Learning strategies for ProtoDUNE raw data denoising
This addresses noise reduction in detector signals for neutrino physics experiments, but appears incremental as it builds on existing deep learning methods for a specific domain.
The paper tackled denoising raw simulation data from the ProtoDUNE neutrino experiment by designing two graph neural network architectures to enhance convolutional neural networks, benchmarking them against traditional algorithms and testing hardware accelerators for speed improvements.
In this work, we investigate different machine learning-based strategies for denoising raw simulation data from the ProtoDUNE experiment. The ProtoDUNE detector is hosted by CERN and it aims to test and calibrate the technologies for DUNE, a forthcoming experiment in neutrino physics. The reconstruction workchain consists of converting digital detector signals into physical high-level quantities. We address the first step in reconstruction, namely raw data denoising, leveraging deep learning algorithms. We design two architectures based on graph neural networks, aiming to enhance the receptive field of basic convolutional neural networks. We benchmark this approach against traditional algorithms implemented by the DUNE collaboration. We test the capabilities of graph neural network hardware accelerator setups to speed up training and inference processes.