Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay-Line Detectors

Accurate observation of two or more particles within a very narrow time window has always been a great challenge in modern physics. It opens the possibility for correlation experiments, as e.g. the important Hanbury Brown-Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a micro-channel plate with subsequent delay-lines for the readout of the incident particle hits. With such a Delay-Line Detector the spatial and temporal coordinates of more than one particle can be fully reconstructed as soon as both particles have a larger separation than what is called the dead radius. For events where two electrons are closer in space and time, the determination of the individual positions of the particles requires elaborated peak finding algorithms. While classical methods work well with single particle hits, they fail to identify and reconstruct events caused by multiple particles when they arrive close in space and time. To address this challenge, a new spatiotemporal machine learning model is developed to identify and reconstruct the position and time of such multi-hit signals. The model achieves a much better resolution for near-by particle hits compared to the classical approaches, reducing the dead radius by half. This shows that machine learning models can be effective in improving the spatiotemporal performance of Delay-Line Detectors.