Artifact segmentation using the U-Net architecture for powder X-ray diffraction images

Abstract

Modern synchrotron X‐ray facilities generate X‐ray diffraction (XRD) image data at rates that far exceed the capacity of manual analysis, yet critical processing steps such as artifact removal rely on human intervention. Rule‐based algorithms in standard beamline software cannot reliably distinguish harmful artifacts like single‐crystal diffraction spots from desirable features like preferred orientation, which leads to incorrectly processed data. We demonstrate that a U‐Net convolutional neural network trained on expert‐labeled experimental data can help automate artifact segmentation in time‐resolved XRD measurements of battery materials. To address overfitting from redundant time‐series frames, we introduce a mutual information‐based pruning algorithm that selects maximally diverse training images. Our optimized model achieves an 85.1% true positive rate on a fully held‐out battery dataset while reducing false positives by 34% compared with GSAS‐II. Critically, the model preserves preferred orientation features that GSAS‐II incorrectly removes, preventing systematic underestimation of phase intensities in downstream analysis.