Selecting stopping muons with KM3NeT/ORCA
International audience The KM3NeT collaboration operates two water Cherenkov neutrino telescopes in the Mediterranean sea, ORCA and ARCA. The flux of atmospheric muons produced in cosmic ray air showers forms a background to the main objectives of KM3NeT/ORCA and KM3NeT/ARCA, respectively measuring...
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ftccsdartic:oai:HAL:hal-04181700v1 2023-09-26T15:21:45+02:00 Selecting stopping muons with KM3NeT/ORCA Bailly-Salins, Louis Laboratoire de physique corpusculaire de Caen (LPCC) Université de Caen Normandie (UNICAEN) Normandie Université (NU)-Normandie Université (NU)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN) Normandie Université (NU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS) KM3NeT Nagoya, Japan 2023-07-26 https://hal.science/hal-04181700 https://doi.org/10.22323/1.444.0203 en eng HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.22323/1.444.0203 hal-04181700 https://hal.science/hal-04181700 doi:10.22323/1.444.0203 INSPIRE: 2681609 PoS 38th International Cosmic Ray Conference https://hal.science/hal-04181700 38th International Cosmic Ray Conference, Jul 2023, Nagoya, Japan. pp.203, ⟨10.22323/1.444.0203⟩ muon atmosphere neutrino detector decay showers oscillation KM3NeT string flux cosmic radiation air water electron optical background crossing Cherenkov machine learning [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] info:eu-repo/semantics/conferenceObject Conference papers 2023 ftccsdartic https://doi.org/10.22323/1.444.0203 2023-08-26T22:28:51Z International audience The KM3NeT collaboration operates two water Cherenkov neutrino telescopes in the Mediterranean sea, ORCA and ARCA. The flux of atmospheric muons produced in cosmic ray air showers forms a background to the main objectives of KM3NeT/ORCA and KM3NeT/ARCA, respectively measuring atmospheric neutrino oscillations and detecting neutrinos from astrophysical sources. A small portion of the atmospheric muons stops inside the detector’s instrumented volume. The stopping muons are 5% of the muons reconstructed using the 6 first strings deployed for ORCA. This still amounts to 1000 events per hour. We present two methods for selecting them, applied on both simulations and data. The first method uses simple cuts on a set of reconstructed variables. The second method uses a machine learning model to classify muons as “stopping” or “crossing”. Both methods allow to reach a high selection purity, close to 95%. Detecting stopping muons can serve many purposes like studying muon decay via the detection of Michel electrons or estimating the flux of atmospheric muons at sea level. This work highlights the accurate reconstruction capabilities of ORCA. The median error on the reconstructed stopping point of selected muons is less than 5 meters, and the median angular deviation is 1° . This is to be compared with the 20 meters horizontal distance between strings and the 9 meters vertical distance between optical modules. Another important result is the excellent agreement between distribution of stopping muons selected in data and in simulations.} Conference Object Orca Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Proceedings of 38th International Cosmic Ray Conference — PoS(ICRC2023) 203 |
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Open Polar |
collection |
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
English |
topic |
muon atmosphere neutrino detector decay showers oscillation KM3NeT string flux cosmic radiation air water electron optical background crossing Cherenkov machine learning [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] |
spellingShingle |
muon atmosphere neutrino detector decay showers oscillation KM3NeT string flux cosmic radiation air water electron optical background crossing Cherenkov machine learning [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] Bailly-Salins, Louis Selecting stopping muons with KM3NeT/ORCA |
topic_facet |
muon atmosphere neutrino detector decay showers oscillation KM3NeT string flux cosmic radiation air water electron optical background crossing Cherenkov machine learning [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] |
description |
International audience The KM3NeT collaboration operates two water Cherenkov neutrino telescopes in the Mediterranean sea, ORCA and ARCA. The flux of atmospheric muons produced in cosmic ray air showers forms a background to the main objectives of KM3NeT/ORCA and KM3NeT/ARCA, respectively measuring atmospheric neutrino oscillations and detecting neutrinos from astrophysical sources. A small portion of the atmospheric muons stops inside the detector’s instrumented volume. The stopping muons are 5% of the muons reconstructed using the 6 first strings deployed for ORCA. This still amounts to 1000 events per hour. We present two methods for selecting them, applied on both simulations and data. The first method uses simple cuts on a set of reconstructed variables. The second method uses a machine learning model to classify muons as “stopping” or “crossing”. Both methods allow to reach a high selection purity, close to 95%. Detecting stopping muons can serve many purposes like studying muon decay via the detection of Michel electrons or estimating the flux of atmospheric muons at sea level. This work highlights the accurate reconstruction capabilities of ORCA. The median error on the reconstructed stopping point of selected muons is less than 5 meters, and the median angular deviation is 1° . This is to be compared with the 20 meters horizontal distance between strings and the 9 meters vertical distance between optical modules. Another important result is the excellent agreement between distribution of stopping muons selected in data and in simulations.} |
author2 |
Laboratoire de physique corpusculaire de Caen (LPCC) Université de Caen Normandie (UNICAEN) Normandie Université (NU)-Normandie Université (NU)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN) Normandie Université (NU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS) KM3NeT |
format |
Conference Object |
author |
Bailly-Salins, Louis |
author_facet |
Bailly-Salins, Louis |
author_sort |
Bailly-Salins, Louis |
title |
Selecting stopping muons with KM3NeT/ORCA |
title_short |
Selecting stopping muons with KM3NeT/ORCA |
title_full |
Selecting stopping muons with KM3NeT/ORCA |
title_fullStr |
Selecting stopping muons with KM3NeT/ORCA |
title_full_unstemmed |
Selecting stopping muons with KM3NeT/ORCA |
title_sort |
selecting stopping muons with km3net/orca |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04181700 https://doi.org/10.22323/1.444.0203 |
op_coverage |
Nagoya, Japan |
genre |
Orca |
genre_facet |
Orca |
op_source |
PoS 38th International Cosmic Ray Conference https://hal.science/hal-04181700 38th International Cosmic Ray Conference, Jul 2023, Nagoya, Japan. pp.203, ⟨10.22323/1.444.0203⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.22323/1.444.0203 hal-04181700 https://hal.science/hal-04181700 doi:10.22323/1.444.0203 INSPIRE: 2681609 |
op_doi |
https://doi.org/10.22323/1.444.0203 |
container_title |
Proceedings of 38th International Cosmic Ray Conference — PoS(ICRC2023) |
container_start_page |
203 |
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1778147061843099648 |