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

TL;DR

This work tackles the challenge of reconstructing multi-hit events in Delay Line Detectors where particle signals overlap in space and time. It introduces a machine-learning pipeline combining a Hit Multiplicity Classifier and a Deep Double Peak Finder, trained on simulated multi-hit data and validated on real measurements, to improve simultaneous-hit resolution beyond classical CFD and peak-fitting methods. The results show a substantial reduction in dead radius (down to ~$2.5\ \text{mm}$ in real data) and lower artifacts, with MCP-specific peak localization reaching RMSE ~$0.17\ \text{ns}$. The approach offers a practical path to enhanced spatiotemporal measurements for correlation experiments and ultrafast electron/ion imaging, with plans to extend to higher multiplicities and online implementation.

Abstract

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.

Figures (19)

• Figure 1: Experimental setup for measuring multi-electron events with a Delay Line Detector. Triggered by ultrashort laser pulses, the needle tip emits one or more electrons that hit the Microchannel Plate (MCP), intensified by secondary electron emission. The resulting bunch passes the delay lines and induces a voltage pulse, producing the data for 6 channels (Ch1--6) and the MCP signal. After amplification the data is discretized by the analog-to-digital converter (ADC).
• Figure 2: (a) Typical time trace of one electron hitting the Delay Line Detector. 7 channels are recorded, consisting of 6 delay line signals and one Microchannel Plate Detector signal. (b) Two-electron event, where two MCP peaks are merged into a single peak, as can be seen by the shoulder to the left. For each event, data recording starts 9 ns before the trigger threshold is reached, as indicated by the arrow and the red star.
• Figure 3: Principle of the constant fraction discriminator (CFD): An incoming pulse is split into two parts. The upper one is delayed, the lower one is inverted and multiplied by a factor $<1$. Both pulses are combined and sent into two comparator circuits. A NIM pulse is created whose rising edge contains the time information.
• Figure 4: (a) Error of Constant Fraction Discriminator (CFD)-based double-hit evaluation. Below $\Delta T\approx23$ ns only one peak position can be found. The green data show the error of the first peak, the brown of the second. The curve was created by application of the CFD principle to a norm pulse in a simulation. The gray area shows the time differences where the CFD only finds one peak. (b) Different examples of double hits, created by the sum of two norm pulses with a time delay of 10 ns (blue, 1), 20 ns (orange, 2) and 25 ns(yellow, 3). Their respective governed peak positions are shown in (a). In both (a) and (b) the amplitudes of the two peaks were equal. (c) Error of the first peak as function of relative peak height difference and time difference $\Delta t$. (d) Same map as (c) but now for the second peak. The white area means that no second peak was found.
• Figure 5: (a) Absolute error on the first peak for a constructed double hit using two norm pulses evaluated with the fit-based classical algorithm. (b) Absolute error on the second peak by the fit-based classical algorithm.