Automatic bad‐pixel mask maker for X‐ray pixel detectors with application to serial crystallography

X‐ray crystallography has witnessed a massive development over the past decade, driven by large increases in the intensity and brightness of X‐ray sources and enabled by employing high‐frame‐rate X‐ray detectors. The analysis of large data sets is done via automatic algorithms that are vulnerable to imperfections in the detector and noise inherent with the detection process. By improving the model of the behaviour of the detector, data can be analysed more reliably and data storage costs can be significantly reduced. One major requirement is a software mask that identifies defective pixels in diffraction frames. This paper introduces a methodology and program based upon concepts of machine learning, called robust mask maker (RMM), for the generation of bad‐pixel masks for large‐area X‐ray pixel detectors based on modern robust statistics. It is proposed to discriminate normally behaving pixels from abnormal pixels by analysing routine measurements made with and without X‐ray illumination. Analysis software typically uses a Bragg peak finder to detect Bragg peaks and an indexing method to detect crystal lattices among those peaks. Without proper masking of the bad pixels, peak finding methods often confuse the abnormal values of bad pixels in a pattern with true Bragg peaks and flag such patterns as useful regardless, leading to storage of enormous uninformative data sets. Also, it is computationally very expensive for indexing methods to search for crystal lattices among false peaks and the solution may be biased. This paper shows how RMM vastly improves peak finders and prevents them from labelling bad pixels as Bragg peaks, by demonstrating its effectiveness on several serial crystallography data sets.

Attention is focused on perhaps the biggest bottleneck in data analysis for serial crystallography at X‐ray free‐electron lasers, which has not received serious enough examination to date. An effective and reliable way is presented to identify anomalies in detectors, using machine learning and recently developed mathematical methods in the field referred to as `robust statistics'. image