Improving the temporal resolution of event-based electron detectors using neural network cluster analysis

• Main Authors: Schröder, Alexander, van Velzen, Leon, Kelder, Maurits, Schäfer, Sascha
• Format: Journal Article
• Language: English
• Published: 31.07.2023
• Subjects: Physics - Instrumentation and Detectors
• DOI: 10.48550/arxiv.2307.16666

Novel event-based electron detector platforms provide an avenue to extend the temporal resolution of electron microscopy into the ultrafast domain. Here, we characterize the timing accuracy of a detector based on a TimePix3 architecture using femtosecond electron pulse trains as a reference. With a large dataset of event clusters triggered by individual incident electrons, a neural network is trained to predict the electron arrival time. Corrected timings of event clusters show a temporal resolution of 2 ns, a 1.6-fold improvement over cluster-averaged timings. This method is applicable to other fast electron detectors down to sub-nanosecond temporal resolutions, offering a promising solution to enhance the precision of electron timing for various electron microscopy applications.