Machine-learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors
Abstract Photomultiplier tubes (PMTs) are widely used in particle experiments for photon detection. PMT waveform analysis is crucial for high-precision measurements of the position and energy of incident particles in liquid scintillator (LS) detectors. A key factor contributing to the energy resolut...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | https://doi.org/10.1140/epjc/s10052-024-13724-3 |
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| _version_ | 1832585429875949568 |
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| author | Wei Jiang Guihong Huang Zhen Liu Wuming Luo Liangjian Wen Jianyi Luo |
| author_facet | Wei Jiang Guihong Huang Zhen Liu Wuming Luo Liangjian Wen Jianyi Luo |
| author_sort | Wei Jiang |
| collection | DOAJ |
| description | Abstract Photomultiplier tubes (PMTs) are widely used in particle experiments for photon detection. PMT waveform analysis is crucial for high-precision measurements of the position and energy of incident particles in liquid scintillator (LS) detectors. A key factor contributing to the energy resolution in large liquid scintillator detectors with PMTs is the charge smearing of PMTs. This paper presents a machine-learning-based photon counting method for PMT waveforms and its application to the energy reconstruction, using the JUNO experiment as an example. The results indicate that leveraging the photon counting information from the machine learning model can partially mitigate the impact of PMT charge smearing and lead to a relative 2.0–2.8% improvement on the energy resolution in the energy range of [1, 9] MeV. |
| format | Article |
| id | doaj-art-7473507df77c4a42b3bed4dbd677d05c |
| institution | Kabale University |
| issn | 1434-6052 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Physical Journal C: Particles and Fields |
| spelling | doaj-art-7473507df77c4a42b3bed4dbd677d05c2025-01-26T12:49:14ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60522025-01-0185111410.1140/epjc/s10052-024-13724-3Machine-learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectorsWei Jiang0Guihong Huang1Zhen Liu2Wuming Luo3Liangjian Wen4Jianyi Luo5School of Physical Sciences, University of Chinese Academy of ScienceWuyi UniversityInstitute of High Energy Physics, Chinese Academy of SciencesInstitute of High Energy Physics, Chinese Academy of SciencesInstitute of High Energy Physics, Chinese Academy of SciencesWuyi UniversityAbstract Photomultiplier tubes (PMTs) are widely used in particle experiments for photon detection. PMT waveform analysis is crucial for high-precision measurements of the position and energy of incident particles in liquid scintillator (LS) detectors. A key factor contributing to the energy resolution in large liquid scintillator detectors with PMTs is the charge smearing of PMTs. This paper presents a machine-learning-based photon counting method for PMT waveforms and its application to the energy reconstruction, using the JUNO experiment as an example. The results indicate that leveraging the photon counting information from the machine learning model can partially mitigate the impact of PMT charge smearing and lead to a relative 2.0–2.8% improvement on the energy resolution in the energy range of [1, 9] MeV.https://doi.org/10.1140/epjc/s10052-024-13724-3 |
| spellingShingle | Wei Jiang Guihong Huang Zhen Liu Wuming Luo Liangjian Wen Jianyi Luo Machine-learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors European Physical Journal C: Particles and Fields |
| title | Machine-learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors |
| title_full | Machine-learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors |
| title_fullStr | Machine-learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors |
| title_full_unstemmed | Machine-learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors |
| title_short | Machine-learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors |
| title_sort | machine learning based photon counting for pmt waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors |
| url | https://doi.org/10.1140/epjc/s10052-024-13724-3 |
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