  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.
