| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 09042 | |
| Number of page(s) | 5 | |
| Section | Artificial Intelligence and Machine Learning | |
| DOI | https://doi.org/10.1051/epjconf/202429509042 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429509042
Vertex reconstruction with Graph Neural Network in JSNS2
Department of Physics, Kyung Hee University, Seoul 02447, KOREA
* e-mail: yewzzang@khu.ac.kr
** e-mail: jhgoh@khu.ac.kr
Published online: 6 May 2024
The JSNS2(J-PARC Sterile Neutrino Search at the J-PARC Spallation Neutron Source) experiment searches for neutrino oscillations at 24m baseline with the J-PARC’s 3 GeV 1 MW proton beam incident on a mercury target at the Materials and Life science experimental Facility (MLF). The JSNS2detector consists of three cylindrical layers, an innermost neutrino target, an intermediate gamma-catcher, and an outermost veto. The neutrino target is made of 17 tonnes of Gd-loaded LS (Gd-LS) stored in an acrylic vessel, 3.2m(D) 2.5m(H). The detector consists of a total of 120 photomultiplier tubes (PMTs), 96 PMTs for inner and 24 PMTs for veto. In JSNS2, a maximum likelihood method based on the PMT charges is used to reconstruct position and energy of the event. We introduce Static Graph Convolution Neural Network (SGCNN), which is a combined model of PointNet and Graph Neural Network (GNN). The model was trained by Monte Carlo (MC) samples, and the position and charge of 96 inner PMTs was used as the training feature.
© The Authors, published by EDP Sciences, 2024
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