Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay
  Line Detectors
Marco Knipfer Stefan Meier Jonas Heimerl Peter Hommelhoff Sergei Gleyzer
Accurate observation of two or more particles within a very narrow time
window has always been a challenge in modern physics. It creates the
possibility of correlation experiments, such as the ground-breaking Hanbury
Brown-Twiss experiment, leading to new physical insights. For low-energy
electrons, one possibility is to use a microchannel plate with subsequent delay
lines for the readout of the incident particle hits, a setup called a Delay
Line Detector. The spatial and temporal coordinates of more than one particle
can be fully reconstructed outside a region called the dead radius. For
interesting events, where two electrons are close in space and time, the
determination of the individual positions of the electrons requires elaborate
peak finding algorithms. While classical methods work well with single particle
hits, they fail to identify and reconstruct events caused by multiple nearby
particles. To address this challenge, we present a new spatiotemporal machine
learning model to identify and reconstruct the position and time of such
multi-hit particle signals. This model achieves a much better resolution for
nearby particle hits compared to the classical approach, removing some of the
artifacts and reducing the dead radius by half. We show that machine learning
models can be effective in improving the spatiotemporal performance of delay
line detectors.