Speaker
Description
The Jiangmen Underground Neutrino Observatory~(JUNO) is currently the world's largest liquid scintillator detector, designed to address fundamental questions in neutrino physics and astrophysics.
During its commissioning phase, the detector was filled with ultra-pure water, functioning as a Cherenkov detector. The additional directional information provided valuable opportunities for the study of \ce{^{8}B} solar neutrinos.
However, this setup also presented unique challenges for MeV-scale event reconstruction, primarily due to the low photon yield of Cherenkov light and the high dark noise rate~($\sim$20 kHz) of the photomultiplier tubes.
To address these challenges, we developed a maximum likelihood-based reconstruction algorithm capable of extracting vertex, direction, and energy information from the noisy background.
The performance of the algorithm was rigorously evaluated using $\gamma$-rays from \ce{^{241}Am}-\ce{^{9}Be} and \ce{^{241}Am}-\ce{^{13}C} calibration sources, where the neutron capture signal was utilized as a coincidence tag to select pure $\gamma$ samples.
By applying this reconstruction to approximately \SI{18}{h} of data, we successfully identified a directional excess correlated with the position of the Sun.
The indication of solar neutrino candidates serves as a definitive \textit{in-situ} validation of the reconstruction strategy and demonstrates the algorithmic capability to mitigate high-rate background noise, a key requirement for handling \ce{^{14}C} pile-up and ensuring high energy resolution in the subsequent LS phase.