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arXiv · 2509.26273

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

Martino Borsato·Giovanni Laganà·Maurizio Martinelli
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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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Martino Borsato, Giovanni Laganà, Maurizio Martinelli. 2025-09-30. Rings of Light, Speed of AI: YOLO for Cherenkov Reconstruction. https://arxiv.org/abs/2509.26273

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