AI-based particle track identification in scintillating fibres read out with imaging sensors
This work addresses particle detection and tracking in physics experiments, offering a fast inference tool for real-time anomaly detection, though it is incremental as it applies an existing AI method to a specific sensor setup.
The paper tackled the problem of identifying particle tracks in data from scintillating fibres with imaging sensors by developing a variational autoencoder (VAE) trained on background frames, which demonstrated high capability in distinguishing signal from noise with rapid processing time.
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.