An interpretable unsupervised representation learning for high precision measurement in particle physics

[Submitted on 27 Nov 2025 (v1), last revised 12 Jun 2026 (this version, v2)]

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Abstract: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 $3,mumathrm{m}$ 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.

Submission history

From: Dexing Miao [view email]
[v1] Thu, 27 Nov 2025 09:18:44 UTC (16,932 KB)
[v2] Fri, 12 Jun 2026 06:37:02 UTC (16,929 KB)