https://doi.org/10.1140/epjc/s10052-022-11066-6
Regular Article - Experimental Physics
Application of transfer learning to neutrino interaction classification
1
Department of Physics, University of Cambridge, CB3 0HE, Cambridge, UK
2
Department of Physics, University of Warwick, CV4 7AL, Coventry, UK
a
andrew.chappell@warwick.ac.uk
b
leigh.howard.whitehead@cern.ch
Received:
8
August
2022
Accepted:
23
November
2022
Published online:
6
December
2022
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 0.8360.004 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.
Andrew Chappell and Leigh H. Whitehead have contributed equally to this work.
© The Author(s) 2022
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Funded by SCOAP3. SCOAP3 supports the goals of the International Year of Basic Sciences for Sustainable Development.
