Vertex reconstruction of neutrino interactions using deep learning

• Autores: Terwilliger A.M., Perdue G.N., Isele D., Patton R.M., Young S.R.
• Formato: objeto de conferencia
• Fecha de Publicación: 2017
• Institución: Consejo Nacional de Ciencia Tecnología e Innovación
• Repositorio: CONCYTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.concytec.gob.pe:20.500.12390/817
• Enlace del recurso: https://hdl.handle.net/20.500.12390/817; https://doi.org/10.1109/IJCNN.2017.7966131
• Nivel de acceso: acceso abierto
• Materia: Vertex reconstruction; Elementary particles; Neutrons; Semantics; Algorithm engineering; Error prones; Learning models; Measurements of; Neutrino interactions; Semantic features; Vertex locations; Deep learning

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.