June 22, 2020 to July 2, 2020
US/Central timezone

Detection of Cosmic Muon Spallation Background in LS-Detector Using Machine Learning

Not scheduled


Mr Zhenghao Fu (MIT)


Neutrinos are the most abundant but also the most mysterious fermions in the universe. In rare event searches like those for neutrinoless double-beta decay (0νββ), one of major backgrounds is caused by cosmic muon spallation. To remove these background events, a precise method with high efficiency is required to separate them from signal events. Machine learning offers a solution to this problem. More specifically, for the spherical detector in KamLAND-Zen, convolutional neural networks (CNNs) based on a spherical system provides a way to classify the data. Besides the classification, a method related to regional CNNs is also developed, aiming to reconstruct the direction of particles in a detector. This poster will cover the concept and usage of spherical CNNs for KamLAND-Zen's data, and the current performance of the direction-determination using simulations.


For KamLAND-like detectors, machine learning can help identify both events and their directions.

Experiment/Collaboration KamLAND-Zen

Primary author

Mr Zhenghao Fu (MIT)

Presentation materials