AI-based particle track identification in scintillating fibres read out with imaging sensors

• URL: http://arxiv.org/abs/2410.10519v1
• Date: Mon, 14 Oct 2024 13:59:30 GMT
• Title: AI-based particle track identification in scintillating fibres read out with imaging sensors
• Authors: Noemi Bührer, Saúl Alonso-Monsalve, Matthew Franks, Till Dieminger, Davide Sgalaberna,
• Abstract summary: 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.
• Score: 1.321203201549798
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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