Bonn 2020 – wissenschaftliches Programm
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T: Fachverband Teilchenphysik
T 101: Neutrino physics without accelerators VII
T 101.6: Vortrag
Freitag, 3. April 2020, 12:15–12:30, H-ÜR 1
Waveform Reconstruction with Deep Learning method in JUNO — •Yu Xu1,2, Christoph Christoph Genster1, Alexandre Göttel1,2, Yuhang Guo1,3, Philipp Kampmann1,2, Runxuan Liu1,2, Ludhova Livia1,2, Giulio Settanta1, and Cornelius Vollbrecht1,2 — 1Institut für Kernphysik, Forschungszentrum Jülich — 2III. Physikalisches Institut B, RWTH Aachen University — 3School of Nuclear Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
Jiangmen Underground Neutrino Experiment (JUNO) is a next generation liquid scintillator neutrino experiment. The main goal of JUNO is to measure the neutrino mass ordering, while its 20 kton target mass and excellent energy resolution of 3%@1MeV will allow to study the neutrinos from multiple sources, including solar, geo, supernova, and atmospheric neutrinos. Signal from about 18,000 20-inch photomultiplier tubes will be read by 1 GHz Flash ADCs. Ideally, the precise reconstruction of charge and hit times of incident photons from Flash ADC waveforms would allow us to push the resolutions of energy and spatial reconstructions to their physical limits: a feature helpful to multiple physics purposes. In this talk, we will present the current status of the waveform reconstruction in JUNO, including the new results obtained with dedicated neural network. The details and the possible ways of additional improvement in waveform reconstruction with deep learning methods will be also discussed.