Reconstructing Time-of-Flight Detector Values of Angular Streaking Using Machine Learning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915403524997120 |
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| author | Meier, David Helml, Wolfram Otto, Thorsten Sick, Bernhard Viefhaus, Jens Hartmann, Gregor |
| author_facet | Meier, David Helml, Wolfram Otto, Thorsten Sick, Bernhard Viefhaus, Jens Hartmann, Gregor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08966 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reconstructing Time-of-Flight Detector Values of Angular Streaking Using Machine Learning Meier, David Helml, Wolfram Otto, Thorsten Sick, Bernhard Viefhaus, Jens Hartmann, Gregor Data Analysis, Statistics and Probability Instrumentation and Detectors 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. |
| title | Reconstructing Time-of-Flight Detector Values of Angular Streaking Using Machine Learning |
| topic | Data Analysis, Statistics and Probability Instrumentation and Detectors |
| url | https://arxiv.org/abs/2501.08966 |