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
The continuous advancements in AI research, particularly in unsupervised learning techniques, are driving new applications in specialized scientific fields like particle physics.
This development represents improved precision and interpretability in critical scientific measurements, which can accelerate discoveries and foundational understanding.
The HistoAE model offers a more interpretable and controllable unsupervised learning approach for high-precision measurement, potentially leading to more reliable and physically sound data analysis in particle physics.
- · Particle physicists
- · High-energy physics research institutions
- · AI researchers specializing in interpretable models
- · Traditional statistical analysis methods
- · AI models lacking interpretability
Improved accuracy and efficiency in data analysis within particle physics experiments.
Accelerated discovery of new particles or phenomena due to enhanced measurement capabilities.
Broader adoption of interpretable AI in other scientific domains requiring high-precision measurements and physical insights.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI