Event reconstruction for KM3NeT/ORCA using convolutional neural networks
Metadatos
Mostrar el registro completo del ítemAutor
Aiello, S.; Anguita López, Mancia; Díaz García, Antonio Francisco; López Coto, Daniel; Navas Concha, Sergio; Tenllado, EnriqueEditorial
IOP Publishing
Materia
Cherenkov detectors Large detector systems for particle and astroparticle physics Neutrino detectors Performance of High Energy Physics Detectors
Fecha
2020-10-08Referencia bibliográfica
Aiello, S. et. al. Event reconstruction for KM3NeT/ORCA using convolutional neural networks. 2020 JINST 15 P10005 [https://doi.org/10.1088/1748-0221/15/10/P10005]
Patrocinador
French National Research Agency (ANR) ANR-15-CE31-0020; Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund); European Union (EU); Institut Universitaire de France (IUF); LabEx UnivEarthS ANR-10-LABX-0023 ANR-18-IDEX-0001; Shota Rustaveli National Science Foundation of Georgia FR-18-1268; German Research Foundation (DFG); Greek Ministry of Development-GSRT; Istituto Nazionale di Fisica Nucleare (INFN); Ministry of Education, Universities and Research (MIUR) Research Projects of National Relevance (PRIN); Ministry of Higher Education, Scientific Research and Professional Training, Morocco; Netherlands Organization for Scientific Research (NWO); National Science Centre, Poland 2015/18/E/ST2/00758; National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades PGC2018-096663-B-C41 A-C42 B-C43 B-C44; Severo Ochoa Centre of Excellence; Junta de Andalucia SOMM17/6104/UGR; Generalitat Valenciana: Grisolia GRISOLIA/2018/119 CIDEGENT/2018/034; La Caixa Foundation LCF/BQ/IN17/11620019; EU: MSC program 713673Resumen
The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.