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

TL;DR

This paper demonstrates a neural network approach to improve the temporal resolution of event-based electron detectors, achieving a 2 ns accuracy and enabling ultrafast electron microscopy.

Contribution

It introduces a neural network-based correction method for event timing, significantly enhancing temporal resolution in electron detectors.

Findings

Achieved 2 ns temporal resolution with neural network correction.

Improved timing accuracy by 1.6 times over traditional cluster-averaged methods.

Applicable to various fast electron detectors for ultrafast microscopy.

Abstract

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.