用Transformer提升南极中微子望远镜的低能重建与分类性能
Enhancing low energy reconstruction and classification in KM3NeT/ORCA with transformers
- 引入物理与探测器设计启发的注意力掩码，让模型理解探测器结构与中微子物理
- 在不同配置间微调时仍能保留关键信息，提升跨配置泛化能力
- 为尚未完工的KM3NeT/ORCA望远镜提供可解释的深度学习解决方案
当前仍在建设中的KM3NeT/ORCA中微子望远镜尚未发挥其全部潜能。训练深度学习模型时，通常不提供物理或探测器的显式信息，导致模型无法理解这些背景。本研究利用Transformer的优势，引入基于物理和探测器设计启发的注意力掩码，使模型能够理解望远镜结构与实测中微子物理。研究还表明，该方法在从一种配置微调到另一种配置时，能有效保留各探测器间的有用信息，显著提升低能中微子重建与分类性能。
原文摘要 · Abstract (English)
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.
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