Publicación:
Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data
Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data
| dc.contributor.author | Song, LH | es_PE |
| dc.contributor.author | Chen, F | es_PE |
| dc.contributor.author | Young, SR | es_PE |
| dc.contributor.author | Schuman, CD | es_PE |
| dc.contributor.author | Perdue, G | es_PE |
| dc.contributor.author | Potok, TE | es_PE |
| dc.date.accessioned | 2024-05-30T23:13:38Z | |
| dc.date.available | 2024-05-30T23:13:38Z | |
| dc.date.issued | 2019 | |
| dc.description | 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). This research was supported in part by an appointment to the Oak Ridge National Laboratory ASTRO Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education. | |
| dc.description.abstract | Se presentó un enfoque de aprendizaje profundo para la reconstrucción de vértices de eventos de interacción neutrino-núcleo, un problema en el dominio de la física de alta energía. En este enfoque, combina datos de energía y tiempo que se recopilan en el detector MIN-ERvA para realizar tareas de clasificación y regresión. Demostramos que la red resultante logra una mayor precisión que los resultados anteriores, mientras que requiere un tamaño de modelo más pequeño y menos tiempo de entrenamiento. En particular, el modelo propuesto supera el estado de la técnica en un 4,00% en precisión de clasificación. | |
| dc.description.sponsorship | Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec | |
| dc.identifier.doi | https://doi.org/10.1109/ICASSP.2019.8683736 | |
| dc.identifier.isi | 482554004024 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12390/1189 | |
| dc.language.iso | eng | |
| dc.publisher | Institute of Electrical and Electronics Engineers | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Física de partículas | |
| dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#1.03.03 | |
| dc.title | Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data | |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dspace.entity.type | Publication |