Speaker
Description
Direction reconstruction in liquid scintillator detectors is challenging because the directionality is due to Cherenkov light, which is typically a small fraction of scintillation light especially with high concentrations of the wavelength shifter. We present a deep learning framework tailored to this regime, based on a purpose-built Dual-Temporal Attention architecture that combines point-cloud representations of detected photon hits, self-attention for learning long-range spatiotemporal correlations, and an adaptive time-gating mechanism designed to emphasize possible Cherenkov-like hits. The method is developed specifically for unordered detector hit patterns, in which directional information is sparse and embedded within overwhelming scintillation backgrounds. Training is performed on approximately 20 million simulated electron events with detector-condition variations, including scintillation rise-time augmentation, to improve robustness against simulation-dependent effects. The model is evaluated on simulated test events and further validated on a sample of real solar-neutrino candidate events from the SNO+ data with full wavelength shifter concentration. In both simulated and real data, the reconstructed direction distributions are consistently biased toward the true direction in both cases, indicating only limited sim-to-real degradation. These results demonstrate that detector-specific attention architectures can recover physically meaningful directional information. This work points to a promising technical pathway for machine-learning-driven reconstruction in neutrino detectors, with broader relevance to sparse, weak-signal inference problems in experimental particle physics.
