Abstract
Liquid argon time projection chambers (LArTPCs) provide dense, high-fidelity 3D measurements of particle interactions and underpin current and future neutrino and rare-event experiments. Physics reconstruction typically relies on complex detector-specific pipelines that use tens of hand-engineered pattern recognition algorithms or cascades of task-specific neural networks that require extensive, labeled simulation that requires a careful, time-consuming calibration process. We introduce \textbf{Panda}, a model that learns reusable sensor-level representations directly from raw unlabeled LArTPC data. Panda couples a hierarchical sparse 3D encoder with a multi-view, prototype-based self-distillation objective. On a simulated dataset, Panda substantially improves label efficiency and reconstruction quality, beating the previous state-of-the-art semantic segmentation model with 1,000$\times$ fewer labels. We also show that a single set-prediction head 1/20th the size of the backbone with no physical priors trained on frozen outputs from Panda can result in particle identification that is comparable with state-of-the-art (SOTA) reconstruction tools. Full fine-tuning further improves performance across all tasks.
Abstract (translated)
液氩时间投影室（LArTPC）提供了粒子相互作用的密集且高保真的三维测量，并支撑着当前和未来的中微子以及罕见事件实验。物理重建通常依赖于复杂的、特定探测器的管道系统，这些系统使用数十种手工设计的模式识别算法或一系列针对具体任务的神经网络组合，后者需要大量标注的模拟数据及细致入微且耗时的校准过程。 我们介绍了一种名为**Panda**的新模型，该模型直接从原始未标记的LArTPC数据中学习可重复使用的传感器级表示。Panda结合了一个层次化的稀疏三维编码器和一个多视角、基于原型的自蒸馏目标函数。在模拟数据集上，Panda大幅提高了标签效率并改善了重建质量，在仅使用当前最先进语义分割模型所需标记数目的千分之一的情况下就超过了之前的记录。 我们还展示了这样一个事实：一个单一集合预测头（其大小仅为骨干网络的1/20）并在没有任何物理先验的前提下训练于Panda冻结输出之上，就能获得与最先进的重建工具相当的粒子识别效果。全面微调则进一步提升了所有任务上的性能表现。
URL
https://arxiv.org/abs/2512.01324
