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.

翻译：无监督学习已被广泛应用于粒子物理的各类任务中。然而，现有模型对其学习到的表示缺乏精确控制，限制了物理可解释性，并阻碍了其在高精度测量中的应用。我们提出直方图自编码器（HistoAE），一种无监督表示学习网络，其核心为基于直方图的自定义损失函数，可强制形成具有物理结构的潜在空间。应用于硅微条探测器时，HistoAE学习到对应于粒子电荷和撞击位置的可解释二维潜在空间。经简单后处理，该模型在束流测试数据上实现了$0.25\,e$的电荷分辨率和$3\,μ\mathrm{m}$的位置分辨率，与常规方法性能相当。这些结果表明，无监督深度学习模型能够实现具有物理意义且定量精确的测量。此外，HistoAE的生成能力使其可便捷地扩展至快速探测器模拟。