AI-based particle track identification in scintillating fibres read out with imaging sensors
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913818106396672 |
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| author | Bührer, Noemi Alonso-Monsalve, Saúl Franks, Matthew Dieminger, Till Sgalaberna, Davide |
| author_facet | Bührer, Noemi Alonso-Monsalve, Saúl Franks, Matthew Dieminger, Till Sgalaberna, Davide |
| contents | This paper presents the development and application of an AI-based method for particle track identification using scintillating fibres read out with imaging sensors. We propose a variational autoencoder (VAE) to efficiently filter and identify frames containing signal from the substantial data generated by SPAD array sensors. Our VAE model, trained on purely background frames, demonstrated a high capability to distinguish frames containing particle tracks from background noise. The performance of the VAE-based anomaly detection was validated with experimental data, demonstrating the method's ability to efficiently identify relevant events with rapid processing time, suggesting a solid prospect for deployment as a fast inference tool on hardware for real-time anomaly detection. This work highlights the potential of combining advanced sensor technology with machine learning techniques to enhance particle detection and tracking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10519 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-based particle track identification in scintillating fibres read out with imaging sensors Bührer, Noemi Alonso-Monsalve, Saúl Franks, Matthew Dieminger, Till Sgalaberna, Davide Machine Learning High Energy Physics - Experiment Instrumentation and Detectors This paper presents the development and application of an AI-based method for particle track identification using scintillating fibres read out with imaging sensors. We propose a variational autoencoder (VAE) to efficiently filter and identify frames containing signal from the substantial data generated by SPAD array sensors. Our VAE model, trained on purely background frames, demonstrated a high capability to distinguish frames containing particle tracks from background noise. The performance of the VAE-based anomaly detection was validated with experimental data, demonstrating the method's ability to efficiently identify relevant events with rapid processing time, suggesting a solid prospect for deployment as a fast inference tool on hardware for real-time anomaly detection. This work highlights the potential of combining advanced sensor technology with machine learning techniques to enhance particle detection and tracking. |
| title | AI-based particle track identification in scintillating fibres read out with imaging sensors |
| topic | Machine Learning High Energy Physics - Experiment Instrumentation and Detectors |
| url | https://arxiv.org/abs/2410.10519 |