Vertex finding in neutrino-nucleus interaction: a model architecture comparison
Abstract We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using...
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Published in | Journal of instrumentation Vol. 17; no. 8; p. T08013 |
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Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Bristol
IOP Publishing
01.08.2022
Institute of Physics (IOP) |
Subjects | |
Online Access | Get full text |
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Summary: | Abstract
We compare different neural network architectures for
machine learning algorithms designed to identify the neutrino
interaction vertex position in the MINERvA detector. The
architectures developed and optimized by hand are compared with the
architectures developed in an automated way using the package
“Multi-node Evolutionary Neural Networks for Deep Learning”
(MENNDL), developed at Oak Ridge National Laboratory. While the
domain-expert hand-tuned network was the best performer, the
differences were negligible and the auto-generated networks
performed as well. There is always a trade-off between human, and
computer resources for network optimization and this work suggests
that automated optimization, assuming resources are available,
provides a compelling way to save significant expert time. |
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Bibliography: | AC02-07CH11359; AC05-00OR22725; SC0008475 USDOE Office of Science (SC), High Energy Physics (HEP) FERMILAB-PUB-22-013-ND-QIS; arXiv:2201.02523 |
ISSN: | 1748-0221 1748-0221 |
DOI: | 10.1088/1748-0221/17/08/T08013 |