Event reconstruction for KM3NeT/ORCA using convolutional neural networks

• Published in: Journal of Instrumentation
• Main Authors: Aiello, S., Albert, A., Alves Garre, S., Aly, Z., Ameli, F., Andre, M., Androulakis, G., Anghinolfi, M., Anguita, M., Anton, G., Ardid, M., Aublin, J., Bagatelas, C., Barbarino, G., Baret, B., Basegmez du Pree, S., Bendahman, M., Berbee, E., Van Den Berg, A.M., Bertin, V., Biagi, S., Biagioni, A., Bissinger, M., Boettcher, M., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Brânzaş, H., Bruijn, R., Brunner, J., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carretero, V., Castaldi, P., Celli, S., Chabab, M., Chau, N., Chen, A., Cherubini, S., Chiarella, V., Chiarusi, T., Circella, M., Cocimano, R., Coelho, J.A.B., Coleiro, A.
• Other Authors: Institut Pluridisciplinaire Hubert Curien (IPHC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Groupe de Recherche en Physique des Hautes Energies (GRPHE), Institut Universitaire de Technologie de Colmar-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Laboratoire de physique subatomique et des technologies associées (SUBATECH), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT), Laboratoire Univers et Particules de Montpellier (LUPM), Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), KM3NeT
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: Cherenkov detectors; Large detector systems for particle and astroparticle physics; Neutrino detectors; Performance of High Energy Physics Detectors; neutrino: detector; neutrino: interaction; neutrino: atmosphere; neutrino: mass; radiation: Cherenkov; vertex: primary; KM3NeT; neural network; Cherenkov counter: water; performance; spatial distribution; charged particle; photomultiplier; background; network; [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]; Orca
• Online Access: https://hal.archives-ouvertes.fr/hal-03178773; https://doi.org/10.1088/1748-0221/15/10/P10005

International audience 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.