Data-driven simultaneous vertex and energy reconstruction for large liquid scintillator detectors

TL;DR

This paper introduces a data-driven, simultaneous vertex and energy reconstruction method for large liquid scintillator detectors like JUNO, improving accuracy by combining charge and time data from PMTs to enhance neutrino detection.

Contribution

It presents a novel data-driven approach for realistic PMT response modeling and a combined vertex-energy reconstruction technique for large liquid scintillator detectors.

Findings

Improved vertex and energy resolution accuracy.

Impact of vertex inaccuracy on energy resolution is about 0.6%.

Enhanced neutrino mass ordering analysis capability.

Abstract

High precision vertex and energy reconstruction is crucial for large liquid scintillator detectors such as JUNO, especially for the determination of the neutrino mass ordering by analyzing the energy spectrum of reactor neutrinos. This paper presents a data-driven method to obtain more realistic and more accurate expected PMT response of positron events in JUNO, and develops a simultaneous vertex and energy reconstruction method that combines the charge and time information of PMTs. For the JUNO detector, the impact of vertex inaccuracy on the energy resolution is about 0.6\%.