SciPost Submission Page
Rings of Light, Speed of AI: YOLO for Cherenkov Reconstruction
by Martino Borsato, Giovanni Laganà, Maurizio Martinelli
Submission summary
| Authors (as registered SciPost users): | Maurizio Martinelli |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2509.26273v1 (pdf) |
| Date submitted: | Oct. 6, 2025, 4:33 p.m. |
| Submitted by: | Maurizio Martinelli |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approach: | Experimental |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
We used ChatGPT (free edition - sept. 2025) to improve the fluency in some points of the text that we originally wrote.
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.
Current status:
Reports on this Submission
Strengths
- Very clear and fluent
- Propose novel Cherenkov Ring ML-based reconstruction algorithm
- Efficient results pave the way toward reconstruction also at HL-LHC era
- Decent performance
Weaknesses
- Still preliminary work.
- Not clear if an algorithm combining pT and ring center information as input to a single neural net will perform well.
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