Vertex reconstruction in the TAO experiment

TL;DR

This paper presents two vertex reconstruction methods, charge center algorithm and deep learning, achieving high precision in the TAO experiment, crucial for accurate neutrino energy spectrum measurement.

Contribution

It introduces and compares two optimized vertex reconstruction algorithms, demonstrating their effectiveness for the TAO experiment and potential for broader application.

Findings

CCA achieves <20mm resolution at 1 MeV

DLA achieves <12mm resolution at 1 MeV

Both meet TAO's vertex reconstruction requirements

Abstract

The Taishan Antineutrino Observatory (TAO) is a tonne-scale gadolinium-doped liquid scintillator satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). It is designed to measure the reactor antineutrino energy spectrum with unprecedented energy resolution, better than 2% at 1 MeV. To fully achieve its designed performance, precise vertex reconstruction is crucial. This work reports two distinct vertex reconstruction methods, the charge center algorithm (CCA) and the deep learning algorithm (DLA). We describe the efforts in optimizing and improving these two methods and compare their reconstruction performance. The results show that the CCA and DLA methods can achieve vertex position resolutions better than 20mm (bias<5mm) and 12mm (bias<1.3mm) at 1 MeV, respectively, fully meeting the requirements of the TAO experiment. The reconstruction algorithms developed in this study not only prepare the TAO experiment for its upcoming real data but also hold significant potential for application in other similar experiments.