Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors
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,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/ad3d2d |
| _version_ | 1827276154184138752 |
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| author | Marco Knipfer Stefan Meier Tobias Volk Jonas Heimerl Peter Hommelhoff Sergei Gleyzer |
| author_facet | Marco Knipfer Stefan Meier Tobias Volk Jonas Heimerl Peter Hommelhoff Sergei Gleyzer |
| author_sort | Marco Knipfer |
| collection | DOAJ |
| description | 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 a factor of eight. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors. |
| first_indexed | 2024-04-24T06:03:41Z |
| format | Article |
| id | doaj.art-e937cdda8a6d44748e2cda613ada362b |
| institution | Directory Open Access Journal |
| issn | 2632-2153 |
| language | English |
| last_indexed | 2024-04-24T06:03:41Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj.art-e937cdda8a6d44748e2cda613ada362b2024-04-23T05:33:17ZengIOP PublishingMachine Learning: Science and Technology2632-21532024-01-015202501910.1088/2632-2153/ad3d2dDeep learning-based spatiotemporal multi-event reconstruction for delay line detectorsMarco Knipfer0https://orcid.org/0000-0001-5103-089XStefan Meier1https://orcid.org/0000-0002-9888-2042Tobias Volk2https://orcid.org/0009-0007-5847-2683Jonas Heimerl3https://orcid.org/0000-0002-7931-8454Peter Hommelhoff4https://orcid.org/0000-0003-4757-5410Sergei Gleyzer5https://orcid.org/0000-0002-6222-8102Chair for Laser Physics, Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) , Staudtstraße 1, 91058 Erlangen, Germany; Department of Physics and Astronomy, University of Alabama , Tuscaloosa, AL 35487, United States of AmericaChair for Laser Physics, Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) , Staudtstraße 1, 91058 Erlangen, GermanyChair for Laser Physics, Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) , Staudtstraße 1, 91058 Erlangen, GermanyChair for Laser Physics, Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) , Staudtstraße 1, 91058 Erlangen, GermanyChair for Laser Physics, Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) , Staudtstraße 1, 91058 Erlangen, GermanyDepartment of Physics and Astronomy, University of Alabama , Tuscaloosa, AL 35487, United States of AmericaAccurate 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 a factor of eight. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors.https://doi.org/10.1088/2632-2153/ad3d2dmachine learningdelaylinedetectorpeak finderneural |
| spellingShingle | Marco Knipfer Stefan Meier Tobias Volk Jonas Heimerl Peter Hommelhoff Sergei Gleyzer Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors Machine Learning: Science and Technology machine learning delay line detector peak finder neural |
| title | Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors |
| title_full | Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors |
| title_fullStr | Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors |
| title_full_unstemmed | Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors |
| title_short | Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors |
| title_sort | deep learning based spatiotemporal multi event reconstruction for delay line detectors |
| topic | machine learning delay line detector peak finder neural |
| url | https://doi.org/10.1088/2632-2153/ad3d2d |
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