Signal Processing and Machine Learning Algorithms for Precise Timing with PICOSEC Micromegas Detectors
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arXiv
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| Format: | Preprint |
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2025
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| author | Kallitsopoulou, A. Maniatis, I. Manthos, I. Papaevangelou, T. Sohl, L. Tsiamis, A. Tzamarias, S. E. |
| author_facet | Kallitsopoulou, A. Maniatis, I. Manthos, I. Papaevangelou, T. Sohl, L. Tsiamis, A. Tzamarias, S. E. |
| contents | High particle rates in current and future experiments make pile-up phenomena a critical issue for extracting useful information. In this context, timing can be important as the 4$^{\mathrm{th}}$ dimension parameter for triggering or event reconstruction. The PICOSEC-Micromegas detector has been shown to offer precise timing of the order of tens of\,ps. In this work, novel signal processing algorithms are being developed and evaluated to demonstrate the technology's ability for online precise timing. We propose, an algorithm based on Artificial Neural Networks (ANN). This algorithm uses a model to train the ANN. The performance of the different algorithms is evaluated using experimental data, resulting in a timing resolution of 18.3 $\pm$ 0.6\,ps, comparable to the standard analysis based on the Constant Fraction Discrimination technique. Additionally, an alternative algorithm using the charge of the pulse exceeding a threshold as a parameter to correct for systematic effects is reported. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13831 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Signal Processing and Machine Learning Algorithms for Precise Timing with PICOSEC Micromegas Detectors Kallitsopoulou, A. Maniatis, I. Manthos, I. Papaevangelou, T. Sohl, L. Tsiamis, A. Tzamarias, S. E. Instrumentation and Detectors High particle rates in current and future experiments make pile-up phenomena a critical issue for extracting useful information. In this context, timing can be important as the 4$^{\mathrm{th}}$ dimension parameter for triggering or event reconstruction. The PICOSEC-Micromegas detector has been shown to offer precise timing of the order of tens of\,ps. In this work, novel signal processing algorithms are being developed and evaluated to demonstrate the technology's ability for online precise timing. We propose, an algorithm based on Artificial Neural Networks (ANN). This algorithm uses a model to train the ANN. The performance of the different algorithms is evaluated using experimental data, resulting in a timing resolution of 18.3 $\pm$ 0.6\,ps, comparable to the standard analysis based on the Constant Fraction Discrimination technique. Additionally, an alternative algorithm using the charge of the pulse exceeding a threshold as a parameter to correct for systematic effects is reported. |
| title | Signal Processing and Machine Learning Algorithms for Precise Timing with PICOSEC Micromegas Detectors |
| topic | Instrumentation and Detectors |
| url | https://arxiv.org/abs/2509.13831 |