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Score-based Graph Generative Model for Neutrino Events Classification and Reconstruction
Yiming Sun · Zixing Song · Irwin King

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in Machine Learning and the Physical Sciences »

The IceCube Neutrino Observatory is an astroparticle physics experiment to investigate neutrinos from the universe. Our task is to classify neutrinos events and reconstruct events of interest. Graph Neural Network (GNN) has achieved great success in this area due to its powerful modeling ability for the irregular grid structure of the detectors. Unlike existing GNN-based methods, which neglect the quality of the constructed graph for the GNN to operate on, we focus on the graph construction step via the score-based generative model to enhance the performance of downstream tasks. Extensive experiments verify the efficacy of our method.

Author Information

Yiming Sun (University of Science and Technology of China)
Zixing Song (The Chinese University of Hong Kong)
Irwin King (Chinese University of Hong Kong)

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