MeV ultrafast electron diffraction (MUED) is a pump-probe technique used to study the dynamic structural evolution of materials. An ultrashort laser pulse triggers structural changes, which are then probed by an ultrashort relativistic electron beam. To overcome low signal-to-noise ratios, diffraction patterns are averaged over thousands of shots. However, shot-to-shot instabilities in the electron beam can distort individual patterns, introducing uncertainty. Improving MUED accuracy requires detecting and removing these anomalous patterns from large datasets. In this work, we developed a fully unsupervised methodology for the detection of anomalous diffraction patterns. Using a convolutional autoencoder, we calculate the reconstruction mean squared error of the diffraction patterns. Based on the statistical analysis of this error, we provide the user an estimation of the probability that the pattern is normal, which also allows a posterior visual inspection of the images that are difficult to classify. This method has been trained with only 100 diffraction patterns and tested on 1521 patterns, resulting in a false positive rate between 0.2\% and 0.4\%, with a training time of 10 seconds per image and a test time of about 1 second per image. The proposed methodology can also be applied to other diffraction techniques in which large datasets are collected that include faulty images due to instrumental instabilities.

翻译：MeV超快电子衍射（MUED）是一种用于研究材料动态结构演化的泵浦-探测技术。超短激光脉冲触发结构变化，随后通过超短相对论电子束进行探测。为克服低信噪比问题，衍射图样需经过数千次曝光进行平均。然而，电子束的逐发不稳定性可能导致单个图样畸变，从而引入不确定性。提高MUED精度需要从大型数据集中检测并移除这些异常图样。本研究开发了一种用于检测异常衍射图样的完全无监督方法。通过卷积自编码器计算衍射图样的重建均方误差，基于该误差的统计分析，我们为用户提供图样正常的概率估计，同时允许对难以分类的图像进行后验视觉检查。该方法仅使用100张衍射图样进行训练，并在1521张图样上完成测试，实现了0.2%至0.4%的误报率，每张图像训练时间为10秒，测试时间约为1秒。所提出的方法也可应用于其他因仪器不稳定性导致大样本数据集中包含缺陷图像的衍射技术。