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

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 clusteraveraged 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.

Automated summary

This study utilizes novel event-based electron detector platforms to extend the temporal resolution of electron microscopy. By training a neural network to predict electron arrival time, the researchers were able to improve the timing accuracy and achieve a promising solution for enhancing electron timing precision in various electron microscopy applications.