SciPost Submission Page
Enhancing low energy reconstruction and classification in KM3NeT/ORCA with transformers
by Iván Mozún Mateo
Submission summary
| Authors (as registered SciPost users): | Iván Mozún Mateo |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2511.18999v1 (pdf) |
| Date submitted: | Dec. 1, 2025, 1:40 p.m. |
| Submitted by: | Iván Mozún Mateo |
| 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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| Approaches: | Experimental, Computational |
Abstract
The current KM3NeT/ORCA neutrino telescope, still under construction, has not yet reached its full potential in neutrino reconstruction capability. When training any deep learning model, no explicit information about the physics or the detector is provided, thus they remain unknown to the model. This study leverages the strengths of transformers by incorporating attention masks inspired by the physics and detector design, making the model understand both the telescope design and the neutrino physics measured on it. The study also shows the efficacy of transformers on retaining valuable information between detectors when doing fine-tuning from one configurations to another.
Current status:
In refereeing
Anonymous on 2025-12-04 [id 6105]
Resubmission properly as an arXiv