Automated Anomaly Detection and Classification in Muon Tracking Data using Multi-Modal Fusion and HyperScore Evaluation

**Abstract:** High-energy physics experiments, particularly those involving muon tracking, generate massive datasets requiring efficient and precise anomaly detection. This paper presents a novel framework, *MuonVision*, that automates anomaly detection and classification within muon tracking data by fusing information from multiple data modalities (tracker hits, calorimetry signals, timing information) and leveraging a hyper-dimensional representation coupled with a rigorous HyperScore evaluation system. MuonVision significantly improves anomaly identification accuracy (projected 30% improvement over baseline models) enabling faster turnaround for physics experiments and improved resolution of rare events, with potential for direct application in future collider upgrades. The method relies solely on established, validated techniques demonstrated over the past decade and avoids speculative future technologies, paving the way for immediate deployment and impact.