Publicación:
Vertex reconstruction of neutrino interactions using deep learning

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dc.contributor.author Terwilliger A.M. es_PE
dc.contributor.author Perdue G.N. es_PE
dc.contributor.author Isele D. es_PE
dc.contributor.author Patton R.M. es_PE
dc.contributor.author Young S.R. es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2017
dc.description.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. en
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1109/IJCNN.2017.7966131
dc.identifier.isbn urn:isbn:9781509061815
dc.identifier.scopus 2-s2.0-85031031977
dc.identifier.uri https://hdl.handle.net/20.500.12390/817
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof Proceedings of the International Joint Conference on Neural Networks
dc.rights info:eu-repo/semantics/openAccess
dc.subject Vertex reconstruction
dc.subject Elementary particles es_PE
dc.subject Neutrons es_PE
dc.subject Semantics es_PE
dc.subject Algorithm engineering es_PE
dc.subject Error prones es_PE
dc.subject Learning models es_PE
dc.subject Measurements of es_PE
dc.subject Neutrino interactions es_PE
dc.subject Semantic features es_PE
dc.subject Vertex locations es_PE
dc.subject Deep learning es_PE
dc.title Vertex reconstruction of neutrino interactions using deep learning
dc.type info:eu-repo/semantics/conferenceObject
dspace.entity.type Publication
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