SMuK 2023 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 87: Neutrinos III

T 87.2: Vortrag

Mittwoch, 22. März 2023, 17:45–18:00, POT/0051

Event Reconstruction in JUNO-TAO using Deep Learning — •Vidhya Thara Hariharan, Daniel Bick, Caren Hagner, and Rosmarie Wirth for the University of Hamburg collaboration — University of Hamburg

he primary goal of JUNO is to resolve the neutrino mass hierarchy using precision spectral measurements of reactor antineutrino oscillations. To achieve this goal a precise knowledge of the unoscillated reactor spectrum is required in order to constrain its fine structure. To account for this, Taishan Antineutrino Observatory (TAO), a ton-level, high energy resolution liquid scintillator detector with a baseline of about 30 m, is set up as a reference detector to JUNO. The 20% increase in the coverage of photosensors, the replacement of Photomultiplier Tubes (PMTs) with Silicon Photomultiplier (SiPM) tiles, the smaller dimension and the operating temperature at -50°C, would enable TAO to achieve a yield of 4,500p.e./MeV. Consequently TAO will achieve an energy resolution better than 2% @ 1 MeV.

The ability to accurately reconstruct reactor antineutrino events in TAO is of great importance for providing a model-independent reference spectrum for JUNO. This work aims to demonstrate the general applicability of Graph Neural Network (GNN) for event reconstruction in TAO. The dataset for model training and validation are Monte Carlo samples generated from the official TAO offline software. The network is trained on the features that are obtained from the information collected by SiPMs to predict the vertices and energy. The resolutions obtained from the model are presented in the talk.