Speaker
Description
Large-volume liquid scintillator detectors produce variable-size sets of PMT(photomultiplier tube) hits with rich timing, charge, and spatial correlations, making track reconstruction a natural application for attention-based architectures. This talk presents Amber (Attention Mechanism Based Event Reconstructor), a self-attention-based framework for cosmic-muon track reconstruction in the Jiangmen Underground Neutrino Observatory (JUNO)—a 20-kt liquid scintillator detector designed primarily to determine the neutrino mass ordering (NMO). Accurate and fast muon reconstruction is vital for JUNO to effectively reject cosmogenic 9Li/8He backgrounds, directly safeguarding the sensitivity of NMO determination and precision physics analyses.
Amber represents each event using hit-level information from fired PMTs, where each hit is encoded by its first-hit time, collected charge, and PMT position, and a Transformer encoder learns global spatiotemporal correlations across the detector response to map the event-level representation directly to muon-track parameters. The framework provides two primary task-specific reconstruction models: Amber-S reconstructs single through-going muons as one straight track, parameterized by a reference point and a direction vector, while Amber-D extends the reconstruction to bundle-muon events using a double-track parameterization with a permutation-invariant loss. Additionally, Amber-OCR serves as an architectural variant of the backbone, specifically designed for high-multiplicity Central Detector PMT inputs by partitioning detector hits into local windows and combining local attention, convolutional feature extraction, and global attention for efficient detector-response modeling.
Trained using a data-driven strategy, Amber directly regresses track parameters from detector hits and achieves an inference time below one second per event in production profiling, demonstrating the feasibility of deploying attention-based reconstruction in large-scale JUNO data processing.