Performance of the muon track reconstruction with the Baikal-GVD neutrino telescope

TL;DR

This paper evaluates the muon track reconstruction performance of the Baikal-GVD neutrino telescope using simulations and real data, highlighting the effectiveness of the algorithms and neutrino selection methods.

Contribution

It presents the muon reconstruction techniques and performance analysis for Baikal-GVD, including the application of machine learning for neutrino event selection.

Findings

Reconstruction algorithms achieve high directional accuracy.

Performance validated with real data and simulations.

Boosted decision tree improves neutrino event classification.

Abstract

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.