Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data

• Autores: Song, LH, Chen, F, Young, SR, Schuman, CD, Perdue, G, Potok, TE
• Formato: objeto de conferencia
• Fecha de Publicación: 2019
• Institución: Consejo Nacional de Ciencia Tecnología e Innovación
• Repositorio: CONCYTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.concytec.gob.pe:20.500.12390/1189
• Enlace del recurso: https://hdl.handle.net/20.500.12390/1189; https://doi.org/10.1109/ICASSP.2019.8683736
• Nivel de acceso: acceso abierto
• Materia: Física de partículas; https://purl.org/pe-repo/ocde/ford#1.03.03

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.