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This research demonstrates how unsupervised deep learning can produce physically interpretable latent spaces for silicon microstrip detectors, achieving measurement precision comparable to traditional methods while enabling faster simulations. By structuring the model's internal representation around fundamental particle properties like charge and impact position, this approach bridges the gap between black-box AI utility and rigorous experimental physics requirements.
An interpretable unsupervised representation learning for high precision measurement in particle physics
arXiv:2511.22246v2 Announce Type: replace-cross 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 $3\,\mu\mathrm{m}$ 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.
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 $3\,\mu\mathrm{m}$ 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.
High Energy Physics - Experiment (hep-ex); Artificial Intelligence (cs.AI); Instrumentation and Detectors (physics.ins-det)
arXiv:2511.22246 [hep-ex]
(or arXiv:2511.22246v2 [hep-ex] for this version)
https://doi.org/10.48550/arXiv.2511.22246 Focus to learn more arXiv-issued DOI via DataCite
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