Observations of track-like neutrino events with Baikal-GVD
- NázevTitle
- Observations of track-like neutrino events with Baikal-GVDObservations of track-like neutrino events with Baikal-GVD
- Druh výsledkuResult type
- Příspěvek ve sborníkuProceedings paper
- AutořiAuthors
- L. Fajt, I. Štekl, F. Šimkovic
- Klíčová slovaKeywords
- astrophysical neutrino, Baikal-GVD
- KonferenceConference
- 37th International Cosmic Ray Conference (Berlin, Germany, 2021-07-15)
- Časopis / citaceJournal / citation
- In: 37th International Cosmic Ray Conference, Proceedings of Science, 2022, pp. 1-8 · ISSN 1824-8039
- RokYear
- 2022
- JazykLanguage
- eng
- ZáznamyRecords
- ProjektProject
- Inženýrské aplikace fyziky mikrosvětaEngineering applications of microworld physics
- Citace ke staženíDownload citation
- TXT · BibTeX
AbstraktAbstract
The Baikal Gigaton Volume Detector (Baikal-GVD) is a km3 -scale neutrino detector currently under construction in Lake Baikal, Russia. The detector currently consists of 2304 optical modules arranged on 64 vertical strings. Further extension of the array is planned for March 2022. The data from the partially complete array have been analyzed using a χ 2 -based track reconstruction algorithm. After suppression of the downward-going atmospheric muon background, a flux of upward-going neutrino events is observed, dominated by the atmospheric neutrinos. The observed flux is in good agreement with Monte Carlo predictions.
The Baikal Gigaton Volume Detector (Baikal-GVD) is a km3 -scale neutrino detector currently under construction in Lake Baikal, Russia. The detector currently consists of 2304 optical modules arranged on 64 vertical strings. Further extension of the array is planned for March 2022. The data from the partially complete array have been analyzed using a χ 2 -based track reconstruction algorithm. After suppression of the downward-going atmospheric muon background, a flux of upward-going neutrino events is observed, dominated by the atmospheric neutrinos. The observed flux is in good agreement with Monte Carlo predictions.