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

arXiv: https://arxiv.org/abs/2306.09359 · HTML: https://mlsanity.com/paper/2306.09359/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 13 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Towards Physics-informed Deep Learning for Turbulent Flow Prediction](https://mlsanity.com/paper/1911.08655/) (2019)
- [Neural Lander: Stable Drone Landing Control using Learned Dynamics](https://mlsanity.com/paper/1811.08027/) (2018)
- [Machine Learning in the Search for New Fundamental Physics](https://mlsanity.com/paper/2112.03769/) (2021)
- [Machine Learning in High Energy Physics Community White Paper](https://mlsanity.com/paper/1807.02876/) (2018)
- [Neural Point Process for Learning Spatiotemporal Event Dynamics](https://mlsanity.com/paper/2112.06351/) (2021)
- [Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems](https://mlsanity.com/paper/2011.10616/) (2020)
- [A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns](https://mlsanity.com/paper/1811.04817/) (2018)
- [DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting](https://mlsanity.com/paper/2102.06684/) (2021)
- [Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data](https://mlsanity.com/paper/1804.00684/) (2018)

## Abstract (arXiv)

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.