基于机器学习的加速器束流成像诊断感兴趣区域分割
Machine Learning Based ROI Segmentation for Beam Imaging Diagnostics at Accelerators
中文总结 AI 辅助
针对传统束流感兴趣区域检测方法在非理想束流轮廓下可靠性不足的问题，开发机器学习方法实现像素级ROI识别，提升了挑战性条件下的鲁棒性与准确性，可应用于各类加速器的束流成像诊断。
中文摘要 AI 辅助
欧洲XFEL加速器利用相对论电子束产生高亮度X射线脉冲，在多个诊断站通过闪烁屏进行束流表征，分析束流图像以提取发射度、能量展宽和电流分布等关键参数。准确检测这些图像中的感兴趣区域（ROI）对可靠的束流诊断和加速器稳定运行至关重要。传统方法如基于边界框的束流定位，在复杂或非理想束流轮廓（如低强度信号、倾斜或条纹状束流、多束流结构）中可靠性会下降。本研究开发了直接从束流图像进行感兴趣区域检测的机器学习（ML）方法，这些ML方法可适应束流形状和强度的变化，实现更精确的像素级束流区域识别。实验结果表明，与传统技术相比，所提方法在挑战性条件下的鲁棒性和准确性均得到提升，该方法已在欧洲XFEL得到验证，可广泛应用于各类加速器设施中基于图像的束流诊断。
英文摘要
The European XFEL accelerator produces high-brightness X-ray pulses using relativistic electron beams. Beam characterization is performed at multiple diagnostic stations using scintillator screens, where beam images are analyzed to extract key parameters such as emittance, energy spread, and current profile. Accurate detection of the region of interest in these images is essential for reliable beam diagnostics and stable accelerator operation. Conventional methods, such as bounding-box-based beam localization, can become less reliable for complex or non-ideal beam profiles, such as low-intensity signals, tilted or streaked beams, and multiple beam structures. In this work, we develop machine learning-based approaches for region of interest detection directly from beam images. These ML methods adapt to variations in beam shape and intensity and enable more precise, pixel-level identification of beam regions. Experimental results demonstrate improved robustness and accuracy in challenging conditions compared to traditional techniques. The proposed methodology is validated at the European XFEL and is broadly applicable to image-based beam diagnostics across accelerator facilities.
