| Article | |
| Report number | arXiv:2103.01596 |
| Title | Deep Learning strategies for ProtoDUNE raw data denoising |
| Author(s) | Rossi, Marco (CERN ; INFN, Milan ; Milan U.) ; Vallecorsa, Sofia (CERN) |
| Publication | 2022-01-07 |
| Imprint | 2021-03-02 |
| Number of pages | 9 |
| Note | 9 pages, 7 figures, 3 tables. Code available at https://github.com/marcorossi5/DUNEdn |
| In: | Comput. Softw. Big Sci. 6, 1 (2022) pp.2 |
| In: | 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.2 |
| DOI | 10.1007/s41781-021-00077-9 |
| Subject category | stat.ML ; Mathematical Physics and Mathematics ; physics.comp-ph ; Other Fields of Physics ; hep-ex ; Particle Physics - Experiment ; cs.LG ; Computing and Computers ; hep-ph ; Particle Physics - Phenomenology |
| Accelerator/Facility, Experiment | ProtoDUNE |
| Abstract | 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. |
| Copyright/License | preprint: (License: arXiv nonexclusive-distrib 1.0) publication: © 2022-2025 The Author(s) (License: CC-BY-4.0) |
Corresponding record in: Inspire
Record created 2021-03-20, last modified 2023-12-06
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