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.

翻译：本文介绍了一种基于人工智能的粒子径迹识别方法的开发与应用，该方法利用成像传感器读出的闪烁光纤进行探测。我们提出了一种变分自编码器（VAE），用于从SPAD阵列传感器生成的海量数据中高效筛选和识别包含信号的帧数据。我们的VAE模型在纯背景帧上训练，展现出卓越的区分粒子径迹帧与背景噪声的能力。基于VAE的异常检测性能已通过实验数据验证，表明该方法能够以快速处理时间高效识别相关事件，为在硬件上部署实时异常检测快速推理工具展现了坚实前景。本工作凸显了将先进传感器技术与机器学习方法相结合以增强粒子探测与追踪能力的潜力。