Vertex reconstruction of neutrino interactions using deep learning

Adam M. Terwilliger; Gabriel N. Perdue; David Isele; Robert M. Patton; Steven R. Young

doi:10.1109/IJCNN.2017.7966131

Vertex reconstruction of neutrino interactions using deep learning

Adam M. Terwilliger, Gabriel N. Perdue, David Isele, Robert M. Patton, Steven R. Young

Learning Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

Deep learning offers new tools to improve our understanding of many important scientific problems. Neutrinos are the most abundant particles in existence and are hypothesized to explain the matter-antimatter asymmetry that dominates our universe. Definitive tests of this conjecture require a detailed understanding of neutrino interactions with a variety of nuclei. Many measurements of interest depend on vertex reconstruction - finding the origin of a neutrino interaction using data from the detector, which can be represented as images. Traditionally, this has been accomplished by utilizing methods that identify the tracks coming from the interaction. However, these methods are not ideal for interactions where an abundance of tracks and cascades occlude the vertex region. Manual algorithm engineering to handle these challenges is complicated and error prone. Deep learning extracts rich, semantic features directly from raw data, making it a promising solution to this problem. In this work, deep learning models are presented that classify the vertex location in regions meaningful to the domain scientists improving their ability to explore more complex interactions.

Original language	English
Title of host publication	2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2275-2281
Number of pages	7
ISBN (Electronic)	9781509061815
DOIs	https://doi.org/10.1109/IJCNN.2017.7966131
State	Published - Jun 30 2017
Event	2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States Duration: May 14 2017 → May 19 2017

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2017-May

Conference

Conference	2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/Territory	United States
City	Anchorage
Period	05/14/17 → 05/19/17

Funding

ACKNOWLEDGEMENTS This work was supported in part by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internship program. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. We would like to thank the MINERvA collaboration for the use of their simulated data and for many useful and stimulating conversations. MINERvA is supported by the Fermi National Accelerator Laboratory under US Department of Energy contract No. DE-AC02-07CH11359 which included the MINERvA construction project. MINERvA construction support was also granted by the United States National Science Foundation under Award PHY-0619727 and by the University of Rochester. Support for participating MINERvA physicists was provided by NSF and DOE (USA), by CAPES and CNPq (Brazil), by CoNaCyT (Mexico), by CONICYT (Chile), by CONCYTEC, DGI-PUCP and IDI/IGIUNI (Peru), and by Latin American Center for Physics (CLAF). Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy.

Funders	Funder number
CLAF
DGI-PUCP
IDI/IGIUNI
Latin American Center for Physics
U. S. Department of Energy
US Department of Energy	DE-AC02-07CH11359
National Science Foundation	PHY-0619727
U.S. Department of Energy	DE-AC05-00OR22725
Office of Science
Workforce Development for Teachers and Scientists
Oak Ridge National Laboratory
Fermilab
University of Rochester
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Agencia Nacional de Investigación y Desarrollo
Consejo Nacional de Ciencia y Tecnología
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica

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10.1109/IJCNN.2017.7966131

Cite this

Terwilliger, A. M., Perdue, G. N., Isele, D., Patton, R. M., & Young, S. R. (2017). Vertex reconstruction of neutrino interactions using deep learning. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings (pp. 2275-2281). Article 7966131 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2017.7966131

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abstract = "Deep learning offers new tools to improve our understanding of many important scientific problems. Neutrinos are the most abundant particles in existence and are hypothesized to explain the matter-antimatter asymmetry that dominates our universe. Definitive tests of this conjecture require a detailed understanding of neutrino interactions with a variety of nuclei. Many measurements of interest depend on vertex reconstruction - finding the origin of a neutrino interaction using data from the detector, which can be represented as images. Traditionally, this has been accomplished by utilizing methods that identify the tracks coming from the interaction. However, these methods are not ideal for interactions where an abundance of tracks and cascades occlude the vertex region. Manual algorithm engineering to handle these challenges is complicated and error prone. Deep learning extracts rich, semantic features directly from raw data, making it a promising solution to this problem. In this work, deep learning models are presented that classify the vertex location in regions meaningful to the domain scientists improving their ability to explore more complex interactions.",

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Terwilliger, AM, Perdue, GN, Isele, D, Patton, RM & Young, SR 2017, Vertex reconstruction of neutrino interactions using deep learning. in 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings., 7966131, Proceedings of the International Joint Conference on Neural Networks, vol. 2017-May, Institute of Electrical and Electronics Engineers Inc., pp. 2275-2281, 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, United States, 05/14/17. https://doi.org/10.1109/IJCNN.2017.7966131

Vertex reconstruction of neutrino interactions using deep learning. / Terwilliger, Adam M.; Perdue, Gabriel N.; Isele, David et al.
2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2275-2281 7966131 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Terwilliger AM, Perdue GN, Isele D, Patton RM , Young SR. Vertex reconstruction of neutrino interactions using deep learning. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2275-2281. 7966131. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2017.7966131

Vertex reconstruction of neutrino interactions using deep learning

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