🤖 AI Summary
This work addresses the challenge of high-resolution reconstruction of beam halo structures in high-energy physics accelerators under conditions of strong noise and severe degradation, where conventional methods encounter performance bottlenecks. The authors propose an unsupervised learning framework that requires no training data, integrating convolutional filtering with neural networks and incorporating an optimized early-stopping strategy to mitigate overfitting. This approach enables robust denoising and high-fidelity reconstruction of beam emittance images at low signal-to-noise ratios. Notably, it achieves high-resolution recovery of beam images without ground-truth labels for the first time, extending measurable amplitudes beyond seven standard deviations and significantly enhancing the resolution of beam halo features—thereby overcoming limitations of existing techniques.
📝 Abstract
Image reconstruction in the presence of severe degradation remains a challenging inverse problem, particularly in beam diagnostics for high-energy physics accelerators. As modern facilities demand precise detection of beam halo structures to control losses, traditional analysis tools have reached their performance limits. This work reviews existing image-processing techniques for data cleaning, contour extraction, and emittance reconstruction, and introduces a novel approach based on convolutional filtering and neural networks with optimized early-stopping strategies in order to control overfitting. Despite the absence of training datasets, the proposed unsupervised framework achieves robust denoising and high-fidelity reconstruction of beam emittance images under low signal-to-noise conditions. The method extends measurable amplitudes beyond seven standard deviations, enabling unprecedented halo resolution.