Deep Learning Strategies for ProtoDUNE Raw Data Denoising

被引：1
作者：
Rossi M. [1 ,2 ]
Vallecorsa S. [1 ]
机构：
[1] CERN openlab, Geneva 23
[2] TIF Lab, Dipartimento di Fisica, Università degli Studi di Milano and INFN Sezione di Milano, Via Celoria 16, Milan
来源：
Computing and Software for Big Science | 2022年 / 6卷 / 1期
关键词：
Convolutional neural networks; Deep learning; Denoising; Graph networks; ProtoDUNE;
D O I：
10.1007/s41781-021-00077-9
中图分类号：
学科分类号：
摘要：
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. © 2022, The Author(s).
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  • [1] Domine L., Terao K., Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data, Phys Rev D, 102, (2020)
  • [2] Abi B., Acciarri R., Et al., Neutrino interaction classification with a convolutional neural network in the DUNE far detector, Phys Rev D, 102, (2020)
  • [3] Aurisano A., Radovic A., Et al., A convolutional neural network neutrino event classifier, J Instrum, 11, 9, (2016)
  • [4] Kronmueller M., Glauch T., Application of Deep Neural Networks to Event Type Classification in Icecube, Application of Deep Neural Networks to Event Type Classification in Icecube, (2019)
  • [5] Albertsson K., Altoe P., Machine Learning in High Energy Physics Community White Paper, Machine Learning in High Energy Physics Community White Paper., (2019)
  • [6] Bourilkov D., Machine and deep learning applications in particle physics, Int J Modern Phys A, 34, 35, (2019)
  • [7] Abi B., Acciarri R., Et al., Volume I. Introduction to DUNE, JINST, 15, 8, (2020)
  • [8] Deep Underground Neutrino Experiment (Dune), Far Detector Technical Design Report, Volume Ii: Dune Physics. Arxiv, 2002, (2020)
  • [9] Abi B., Acciarri R., Et al., Volume III. DUNE far detector technical coordination, JINST, 15, 8, (2020)
  • [10] Abi B., Acciarri R., Et al., Volume IV. The DUNE far detector single-phase technology, JINST, 15, 8, (2020)
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