Machine Learning Based ROI Segmentation for Beam Imaging Diagnostics at Accelerators

Abstract

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.