Presentation
Matrix Sketching for Online Analysis of LCLS Imaging Datasets
DescriptionX-ray light source facilities such as the Linac Coherence Light Source (LCLS) at SLAC National Accelerator Laboratory generate massive amounts of data that need to be analyzed quickly to inform ongoing experiments. However, the high repetition rate and dimensionality of these data streams make their analysis challenging in both scalability and interpretability. In this work, we propose an image-monitoring and classification framework that follows a three-stage process: dimensionality reduction by matrix sketching, visualization using UMAP, and clustering using OPTICS. In the dimensionality reduction step, we combine the Priority Sampling algorithm with a modified Frequent Directions algorithm to produce an accelerated rank-adaptive matrix sketching (ARAMS) algorithm, wherein practitioners specify the target error of the sketch as opposed to the rank. Furthermore, the framework is parallel, enabling real-time analysis of the underpinning structure of the data. We explore its effectiveness on both beam profile data and diffraction data from recent LCLS experiments.
