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Search for cascade events with Baikal-GVD

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 Added by Fedor Simkovic
 Publication date 2019
  fields Physics
and research's language is English




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Baikal-GVD is a next generation, kilometer-scale neutrino telescope currently under construction in Lake Baikal. GVD is formed by multi-megaton sub-arrays (clusters) and is designed for the detection of astrophysical neutrino fluxes at energies from a few TeV up to 100 PeV. The design of the Baikal-GVD allows one to search for astrophysical neutrinos with flux values measured by IceCube already at early phases of the array construction. We present here preliminary results of the search for high-energy neutrinos via the cascade mode with the Baikal-GVD neutrino telescope.



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131 - Dmitry Zaborov 2021
The Baikal Gigaton Volume Detector (Baikal-GVD) is a km$^3$-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 $chi^2$-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 Advanced LIGO and Advanced Virgo observatories recently discovered gravitational waves from a binary neutron star inspiral. A short gamma-ray burst (GRB) that followed the merger of this binary was also recorded by Fermi-GBM and INTEGRAL, indicating particle acceleration by the source. The precise location of the event was determined by optical detections of emission following the merger. We searched for high-energy neutrinos from the merger in the TeV - 100 PeV energy range using Baikal-GVD. No neutrinos directionally coincident with the source were detected within $pm$500 s around the merger time, as well as during a 14-day period after the GW detection. We derived 90% confidence level upper limits on the neutrino fluence from GW170817 during a $pm$500 s window centered on the GW trigger time, and a 14-day window following the GW signal under the assumption of an $E^{-2}$ neutrino energy spectrum.
Baikal-GVD is a neutrino telescope currently under construction in Lake Baikal. GVD is formed by multi-meganton subarrays (clusters). The design of Baikal-GVD allows one to search for astrophysical neutrinos already at early phases of the array construction. We present here preliminary results of a search for high-energy neutrinos with GVD in 2019-2020.
Baikal-GVD is a km$^3$-scale neutrino telescope being constructed in Lake Baikal. Muon and partially tau (anti)neutrino interactions near the detector through the W$^{pm}$-boson exchange are accompanied by muon tracks. Reconstructed direction of the track is arguably the most precise probe of the neutrino direction attainable in Cerenkov neutrino telescopes. Muon reconstruction techniques adopted by Baikal-GVD are discussed in the present report. Performance of the muon reconstruction is studied using realistic Monte Carlo simulation of the detector. The algorithms are applied to real data from Baikal-GVD and the results are compared with simulations. The performance of the neutrino selection based on a boosted decision tree classifier is discussed.
Baikal-GVD is a kilometer scale neutrino telescope currently under construction in Lake Baikal. Due to water currents in Lake Baikal, individual photomultiplier housings are mobile and can drift away from their initial position. In order to accurately determine the coordinates of the photomultipliers, the telescope is equipped with an acoustic positioning system. The system consists of a network of acoustic modems, installed along the telescope strings and uses acoustic trilateration to determine the coordinates of individual modems. This contribution discusses the current state of the positioning in Baikal-GVD, including the recent upgrade to the acoustic modem polling algorithm.
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