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.

翻译：在现代物理学中，在极窄时间窗口内精确观测两个或多个粒子始终是一项挑战。这为关联实验创造了可能性，例如开创性的汉伯里-布朗-特维斯实验，从而带来新的物理见解。对于低能电子，一种可行方案是采用微通道板配合后续延迟线来读取入射粒子撞击信号，这种装置称为延迟线探测器。在称为死区半径的区域之外，可以完全重建多个粒子的空间和时间坐标。对于两个电子在时空上均邻近的有趣事件，确定电子的各自位置需要复杂的峰值查找算法。虽然经典方法能有效处理单粒子撞击，但无法识别和重建由多个邻近粒子引起的事件。为应对这一挑战，我们提出了一种新的时空机器学习模型，用于识别和重建此类多击中粒子信号的位置与时间。相较于经典方法，该模型对邻近粒子撞击实现了更优的分辨率，消除了部分伪影，并将死区半径减小了一半。我们证明，机器学习模型能有效提升延迟线探测器的时空性能。