Speaker
Description
The Jiangmen Underground Neutrino Observation (JUNO), located at Southern China, is a multi-purpose neutrino experiment that consist of a 20-kton liquid scintillator detector. The primary goal of the experiment is to measure the neutrino mass ordering (NMO) and measure the relevant oscillation parameters to a high precision. Atmospheric neutrinos are sensitive to NMO via matter effects and can improve JUNO’s total sensitivity in a joint analysis with reactor neutrinos, in which a good capability of reconstructing atmospheric neutrinos are crucial for such measurements.
In this contribution, we present a machine learning approach for the particle identification of atmospheric neutrinos in JUNO. The method of feature extraction from PMT waveforms that are used as inputs to the machine learning models are detailed. Two strategies of utilising neutron capture information are also discussed. And preliminary results based on Monte-Carlo simulations will be presented. We demonstrate that using Machine Learning-based approach can achieve the required level of accuracy for oscillation-related physics measurements.
| Type of contribution | Talk: 15 minutes. |
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