Rings of Light, Speed of AI: YOLO for Cherenkov Reconstruction

2025/09/30 by Martino Borsato, Borsato, Martino, M. Martinelli +2
Decision Sciences · Medicine · Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Medical Imaging Techniques and Applications #Radiation Detection and Scintillator Technologies #Scientific Computing and Data Management

paper · pdf · doi:10.48550/arxiv.2509.26273

openalex publication_date 2025/09/30 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28

Abstract

Cherenkov rings play a crucial role in identifying charged particles in high-energy physics (HEP) experiments. Most Cherenkov ring pattern reconstruction algorithms currently used in HEP experiments rely on a likelihood fit to the photo-detector response, which often consumes a significant portion of the computing budget for event reconstruction. We present a novel approach to Cherenkov ring reconstruction using YOLO, a computer vision algorithm capable of real-time object identification with a single pass through a neural network. We obtain a reconstruction efficiency above 95% and a pion misidentification rate below 5% across a wide momentum range for all particle species.

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