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

TL;DR

This paper introduces an AI-based approach using a variational autoencoder to efficiently identify particle tracks in scintillating fibres read out with imaging sensors, enabling real-time anomaly detection in experimental data.

Contribution

The paper presents a novel VAE-based method trained on background data to distinguish particle signals from noise in scintillating fibre sensor data.

Findings

High accuracy in detecting particle tracks

Fast processing suitable for real-time applications

Validated with experimental data

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.