Scientific researchers frequently use the in situ synchrotron high-energy
powder X-ray diffraction (XRD) technique to examine the crystallographic
structures of materials in functional devices such as rechargeable battery
materials. We propose a method for identifying artifacts in experimental XRD
images. The proposed method uses deep learning convolutional neural network
architectures, such as tunable U-Nets to identify the artifacts. In particular,
the predicted artifacts are evaluated against the corresponding ground truth
(manually implemented) using the overall true positive rate or recall. The
result demonstrates that the U-Nets can consistently produce great recall
performance at 92.4% on the test dataset, which is not included in the
training, with a 34% reduction in average false positives in comparison to the
conventional method. The U-Nets also reduce the time required to identify and
separate artifacts by more than 50%. Furthermore, the exclusion of the
artifacts shows major changes in the integrated 1D XRD pattern, enhancing
further analysis of the post-processing XRD data.

通过使用深度学习卷积神经网络架构（如可调整的 U-Net），我们提出了一种在实验 XRD 图像中识别伪迹的方法，结果表明 U-Net 在测试数据集上能够一致地产生 92.4% 的召回率，与传统方法相比平均错误率降低了 34%，同时减少了 50% 以上的识别和排除伪迹的时间，进一步分析了后处理 XRD 数据的集成 1D XRD 图案。