Towards neutrino astronomy with tracks in Baikal-GVD

AbstraktAbstract

Baikal-GVD is a gigaton-scale neutrino telescope being constructed in Lake Baikal. The detector presently includes 14 independent detector sub-arrays (clusters), each consisting of 8 or 9 strings, each of which holds 36 optical modules. High-energy muon neutrino interaction through the W-boson exchange results in the production of muons with range of propagation reaching many kilometers. Reconstructing the neutrino-induced muon tracks in the detector provides a measurement of the neutrino direction with precision up to 0.2◦ and beyond, enabling the use of the neutrino data for astronomy. The Baikal-GVD data from data-taking seasons 2019-2023 have been processed with a dedicated track reconstruction algorithm, yielding a sample of few thousand neutrino candidate events. The data sample have been employed to search for astrophysical neutrino diffuse flux and for neutrino emission from point-like sources. In this report we briefly discuss the data reduction algorithms and present the preliminary analysis results.

Baikal-GVD is a gigaton-scale neutrino telescope being constructed in Lake Baikal. The detector presently includes 14 independent detector sub-arrays (clusters), each consisting of 8 or 9 strings, each of which holds 36 optical modules. High-energy muon neutrino interaction through the W-boson exchange results in the production of muons with range of propagation reaching many kilometers. Reconstructing the neutrino-induced muon tracks in the detector provides a measurement of the neutrino direction with precision up to 0.2◦ and beyond, enabling the use of the neutrino data for astronomy. The Baikal-GVD data from data-taking seasons 2019-2023 have been processed with a dedicated track reconstruction algorithm, yielding a sample of few thousand neutrino candidate events. The data sample have been employed to search for astrophysical neutrino diffuse flux and for neutrino emission from point-like sources. In this report we briefly discuss the data reduction algorithms and present the preliminary analysis results.