用自编码器实现粒子物理中可解释的高精度测量
An interpretable unsupervised representation learning for high precision measurement in particle physics
- 设计基于直方图的损失函数，让隐空间具有物理意义
- 电荷分辨率0.25电子，位置精度3微米，接近传统方法
- 适合需要可解释性与高精度的粒子探测场景
无监督学习在粒子物理中应用广泛，但现有模型对学习表征的控制不足，影响物理可解释性，限制其用于精确测量。本文提出直方图自编码器（HistoAE），一种基于定制直方图损失的无监督表征学习网络，强制隐空间具有物理结构。应用于硅微条探测器时，HistoAE学习到对应粒子电荷与撞击位置的可解释二维隐空间。经简单后处理，其在束流测试数据上达到0.25电子的电荷分辨率和3微米的位置分辨率，与传统方法相当。结果表明，无监督深度学习模型可实现物理有意义且定量精确的测量。此外，HistoAE具备生成能力，便于扩展至快速探测器仿真。
原文摘要 · Abstract (English)
Unsupervised learning has been widely applied to various tasks in particle physics. However, existing models lack precise control over their learned representations, limiting physical interpretability and hindering their use for accurate measurements. We propose the Histogram AutoEncoder (HistoAE), an unsupervised representation learning network featuring a custom histogram-based loss that enforces a physically structured latent space. Applied to silicon microstrip detectors, HistoAE learns an interpretable two-dimensional latent space corresponding to the particle's charge and impact position. After simple post-processing, it achieves a charge resolution of $0.25\,e$ and a position resolution of $3\,μ\mathrm{m}$ on beam-test data, comparable to the conventional approach. These results demonstrate that unsupervised deep learning models can enable physically meaningful and quantitatively precise measurements. Moreover, the generative capacity of HistoAE enables straightforward extensions to fast detector simulations.
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