Reconstructing Time-of-Flight Detector Values of Angular Streaking Using Machine Learning
Abstract
Angular streaking experiments enable for experimentation in the attosecond regions. However, the deployed Time-of-flight detectors are susceptible to noise and failure. These shortcomings make the outputs of the Time-of-flight detectors hard to understand for humans and further processing, such as for example the extraction of beam properties. In this article, we present an approach to remove high noise levels and reconstruct up to three failed Time-of-flight detectors from an arrangement of 16 Time-of-flight detectors. Due to its fast evaluation time, the presented method is applicable online during a running experiment. It is trained with simulation data, and we show the results of denoising and reconstruction of our method on real-world experiment data.
Keywords
Cite
@article{arxiv.2501.08966,
title = {Reconstructing Time-of-Flight Detector Values of Angular Streaking Using Machine Learning},
author = {David Meier and Wolfram Helml and Thorsten Otto and Bernhard Sick and Jens Viefhaus and Gregor Hartmann},
journal= {arXiv preprint arXiv:2501.08966},
year = {2025}
}
Comments
Submitted to APS Physical Review Accelerators and Beams