用自监督学习减少中微子探测对仿真数据的依赖，提升真实数据利用效率。
Reducing Simulation Dependence in Neutrino Telescopes with Masked Point Transformers
- 基于点云变换器与掩码自编码器构建自监督训练框架
- 在真实数据上完成主要训练，降低对仿真数据的依赖
- 适合关注数据真实性与系统误差控制的研究者
中微子物理中的机器学习传统依赖模拟数据以获得真实标签，但模拟精度及与真实数据的偏差仍是重大挑战，尤其在复杂自然介质中运行的大规模中微子望远镜。近年来，自监督学习成为减少对标注数据依赖的有力范式。本文首次为中微子望远镜提出自监督训练流程，采用点云变压器与掩码自编码器，将大部分训练转向真实数据，从而显著降低对仿真的依赖，缓解相关系统不确定性。这一方法标志着中微子望远镜机器学习应用的根本转变，为事件重建与分类带来实质性改进。
原文摘要 · Abstract (English)
Machine learning techniques in neutrino physics have traditionally relied on simulated data, which provides access to ground-truth labels. However, the accuracy of these simulations and the discrepancies between simulated and real data remain significant concerns, particularly for large-scale neutrino telescopes that operate in complex natural media. In recent years, self-supervised learning has emerged as a powerful paradigm for reducing dependence on labeled datasets. Here, we present the first self-supervised training pipeline for neutrino telescopes, leveraging point cloud transformers and masked autoencoders. By shifting the majority of training to real data, this approach minimizes reliance on simulations, thereby mitigating associated systematic uncertainties. This represents a fundamental departure from previous machine learning applications in neutrino telescopes, paving the way for substantial improvements in event reconstruction and classification.
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