Reconstructing time-of-flight detector values of angular streaking using machine learning
Angular streaking experiments enable for experimentation in the attosecond regions. However, the deployed time-of-flight (TOF) detectors are susceptible to noise and failure. These shortcomings make the outputs of the TOF detectors hard to understand for humans and further processing, such as, for e...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2025-07-01
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| Series: | Physical Review Accelerators and Beams |
| Online Access: | http://doi.org/10.1103/csvm-858f |
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| _version_ | 1849429871133982720 |
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| author | David Meier Jens Viefhaus Gregor Hartmann Wolfram Helml Thorsten Otto Bernhard Sick |
| author_facet | David Meier Jens Viefhaus Gregor Hartmann Wolfram Helml Thorsten Otto Bernhard Sick |
| author_sort | David Meier |
| collection | DOAJ |
| description | Angular streaking experiments enable for experimentation in the attosecond regions. However, the deployed time-of-flight (TOF) detectors are susceptible to noise and failure. These shortcomings make the outputs of the TOF 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 TOF detectors from an arrangement of 16 TOF 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 | Article |
| id | doaj-art-1f3db875805d4bb185463a89a9c8448b |
| institution | Kabale University |
| issn | 2469-9888 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Accelerators and Beams |
| spelling | doaj-art-1f3db875805d4bb185463a89a9c8448b2025-08-20T03:28:13ZengAmerican Physical SocietyPhysical Review Accelerators and Beams2469-98882025-07-0128707460110.1103/csvm-858fReconstructing time-of-flight detector values of angular streaking using machine learningDavid MeierJens ViefhausGregor HartmannWolfram HelmlThorsten OttoBernhard SickAngular streaking experiments enable for experimentation in the attosecond regions. However, the deployed time-of-flight (TOF) detectors are susceptible to noise and failure. These shortcomings make the outputs of the TOF 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 TOF detectors from an arrangement of 16 TOF 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.http://doi.org/10.1103/csvm-858f |
| spellingShingle | David Meier Jens Viefhaus Gregor Hartmann Wolfram Helml Thorsten Otto Bernhard Sick Reconstructing time-of-flight detector values of angular streaking using machine learning Physical Review Accelerators and Beams |
| title | Reconstructing time-of-flight detector values of angular streaking using machine learning |
| title_full | Reconstructing time-of-flight detector values of angular streaking using machine learning |
| title_fullStr | Reconstructing time-of-flight detector values of angular streaking using machine learning |
| title_full_unstemmed | Reconstructing time-of-flight detector values of angular streaking using machine learning |
| title_short | Reconstructing time-of-flight detector values of angular streaking using machine learning |
| title_sort | reconstructing time of flight detector values of angular streaking using machine learning |
| url | http://doi.org/10.1103/csvm-858f |
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