Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916790204891136 |
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| author | Jenegger, Tobias Hartman, Nicole Gernhaeuser, Roman Heinrich, Lukas Fabbietti, Laura |
| author_facet | Jenegger, Tobias Hartman, Nicole Gernhaeuser, Roman Heinrich, Lukas Fabbietti, Laura |
| contents | The R3B experiment at FAIR studies nuclear reactions using high-energy radioactive beams. One key detector in R3B is the CALIFA calorimeter consisting of 2544 CsI(Tl) scintillator crystals designed to detect light charged particles and gamma rays with an energy resolution in the per cent range after Doppler correction. Precise cluster reconstruction from sparse hit patterns is a crucial requirement. Standard algorithms typically use fixed cluster sizes or geometric thresholds. To enhance performance, advanced machine learning techniques such as agglomerative clustering were implemented to use the full multi-dimensional parameter space including geometry, energy and time of individual interactions. An Edge Detection Neural Network exhibited significant differences. This study, based on Geant4 simulations, demonstrates improvements in cluster reconstruction efficiency of more than 30%, showcasing the potential of machine learning in nuclear physics experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09088 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B Jenegger, Tobias Hartman, Nicole Gernhaeuser, Roman Heinrich, Lukas Fabbietti, Laura Instrumentation and Detectors High Energy Physics - Experiment The R3B experiment at FAIR studies nuclear reactions using high-energy radioactive beams. One key detector in R3B is the CALIFA calorimeter consisting of 2544 CsI(Tl) scintillator crystals designed to detect light charged particles and gamma rays with an energy resolution in the per cent range after Doppler correction. Precise cluster reconstruction from sparse hit patterns is a crucial requirement. Standard algorithms typically use fixed cluster sizes or geometric thresholds. To enhance performance, advanced machine learning techniques such as agglomerative clustering were implemented to use the full multi-dimensional parameter space including geometry, energy and time of individual interactions. An Edge Detection Neural Network exhibited significant differences. This study, based on Geant4 simulations, demonstrates improvements in cluster reconstruction efficiency of more than 30%, showcasing the potential of machine learning in nuclear physics experiments. |
| title | Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B |
| topic | Instrumentation and Detectors High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2506.09088 |