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Application of transfer learning to neutrino interaction classification
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Chappell, Andrew and Whitehead, Leigh H. (2022) Application of transfer learning to neutrino interaction classification. The European Physical Journal C, 82 (12). 1099. doi:10.1140/epjc/s10052-022-11066-6 ISSN 1434-6044.
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Official URL: https://doi.org/10.1140/epjc/s10052-022-11066-6
Abstract
Training deep neural networks using simulations typically requires very large numbers of simulated events. This can be a large computational burden and a limitation in the performance of the deep learning algorithm when insufficient numbers of events can be produced. We investigate the use of transfer learning, where a set of simulated images are used to fine tune a model trained on generic image recognition tasks, to the specific use case of neutrino interaction classification in a liquid argon time projection chamber. A ResNet18, pre-trained on photographic images, was fine-tuned using simulated neutrino images and when trained with one hundred thousand training events reached an F1 score of 0.896±0.002 compared to 0.836±0.004 from a randomly-initialised network trained with the same training sample. The transfer-learned networks also demonstrate lower bias as a function of energy and more balanced performance across different interaction types.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QC Physics | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Neutrino interactions, Neural networks (Computer science), Deep learning (Machine learning) | ||||||
Journal or Publication Title: | The European Physical Journal C | ||||||
Publisher: | Springer | ||||||
ISSN: | 1434-6044 | ||||||
Official Date: | 6 December 2022 | ||||||
Dates: |
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Volume: | 82 | ||||||
Number: | 12 | ||||||
Article Number: | 1099 | ||||||
DOI: | 10.1140/epjc/s10052-022-11066-6 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 9 January 2023 | ||||||
Date of first compliant Open Access: | 9 January 2023 |
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