An Online Data Analysis Framework for Small-Scale Physics Experiments
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arXiv
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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911356706357248 |
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| author | Ramm, Hayden Simon, Pascal Alexaki, Paraskevi Arran, Christopher Bingham, Robert Goillot, Alice Gudmundsson, Jon Tomas Halliday, Jonathan Lloyd, Bryn Los, Eva Stergiou, Vasiliki Zhang, Sifei Gregori, Gianluca Charitonidis, Nikolaos |
| author_facet | Ramm, Hayden Simon, Pascal Alexaki, Paraskevi Arran, Christopher Bingham, Robert Goillot, Alice Gudmundsson, Jon Tomas Halliday, Jonathan Lloyd, Bryn Los, Eva Stergiou, Vasiliki Zhang, Sifei Gregori, Gianluca Charitonidis, Nikolaos |
| contents | A robust and flexible architecture capable of providing real-time analysis on diagnostic data is of crucial importance to physics experiments. In this paper, we present such an online framework, used in June 2025 as part of the HRMT-68 experiment, performed at the HiRadMat facility at CERN, using the Super Proton Synchrotron (SPS) beam line. HRMT-68 was a fixed-target laboratory astrophysics experiment aiming to identify plasma instabilities generated by a relativistic electron-positron beam during traversal of an argon plasma. This framework was essential for experimental data acquisition and analysis, and can be adapted for a broad range of experiments with a variety of experimental diagnostics. The framework's modular and customizable design enabled us to rapidly observe and extract emergent features from a diverse range of diagnostic data. Simultaneously, it allowed for both the introduction of new diagnostic devices and the modification of our analysis as features of interest were identified. As a result, we were able to effectively diagnose equipment malfunction, and infer the beam's response to varying bunch duration, beam intensity, and the plasma state without resorting to offline analysis, at which time adjustment or improvement would have been impossible. We present the features of this agile framework, whose codebase we have made publicly available, which can be adapted for future experiments with minimal modification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00705 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Online Data Analysis Framework for Small-Scale Physics Experiments Ramm, Hayden Simon, Pascal Alexaki, Paraskevi Arran, Christopher Bingham, Robert Goillot, Alice Gudmundsson, Jon Tomas Halliday, Jonathan Lloyd, Bryn Los, Eva Stergiou, Vasiliki Zhang, Sifei Gregori, Gianluca Charitonidis, Nikolaos Accelerator Physics High Energy Physics - Experiment A robust and flexible architecture capable of providing real-time analysis on diagnostic data is of crucial importance to physics experiments. In this paper, we present such an online framework, used in June 2025 as part of the HRMT-68 experiment, performed at the HiRadMat facility at CERN, using the Super Proton Synchrotron (SPS) beam line. HRMT-68 was a fixed-target laboratory astrophysics experiment aiming to identify plasma instabilities generated by a relativistic electron-positron beam during traversal of an argon plasma. This framework was essential for experimental data acquisition and analysis, and can be adapted for a broad range of experiments with a variety of experimental diagnostics. The framework's modular and customizable design enabled us to rapidly observe and extract emergent features from a diverse range of diagnostic data. Simultaneously, it allowed for both the introduction of new diagnostic devices and the modification of our analysis as features of interest were identified. As a result, we were able to effectively diagnose equipment malfunction, and infer the beam's response to varying bunch duration, beam intensity, and the plasma state without resorting to offline analysis, at which time adjustment or improvement would have been impossible. We present the features of this agile framework, whose codebase we have made publicly available, which can be adapted for future experiments with minimal modification. |
| title | An Online Data Analysis Framework for Small-Scale Physics Experiments |
| topic | Accelerator Physics High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2508.00705 |