Speaker
Description
The JSNS$^2$ (J-PARC Sterile Neutrino Search at the J-PARC Spallation Neutron Source) experiment searches for neutrino oscillations at 24m baseline from 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 JSNS$^2$ detector consists of three cylindrical layers including an inner most neutrino target, an intermediate gamma-catcher, and an outermost vetoes. The neutrino target is made of 17 tonnes of Gd loaded LS (Gd-LS) stored in an acrylic vessel, 3.2m(D) x 2.5m(H). The detector consists of a total of 120 photomultiplier tubes (PMTs), 96 PMTs for inner and 24 PMTs for outer veto. In JSNS$^2$, a maximum likelihood method based on the PMT charges is used to reconstruct position and energy of the event. We present the results of the first application of a deep learning model called Dynamic Graph Convolution Neural Network (DGCNN), which is a combined model of PointNet and Graph Neural Network (GNN). The model was trained using the position and charge of 96 inner PMTs.
| Consider for long presentation | No |
|---|
