用AI从闪烁光纤信号中快速识别粒子轨迹，提升实时检测效率。
AI-based particle track identification in scintillating fibres read out with imaging sensors
- 用变分自编码器仅基于背景帧训练，自动过滤信号帧
- 实验验证可高效区分粒子轨迹与噪声，处理速度快
- 适合需要实时异常检测的高能物理实验场景
本文提出一种基于人工智能的粒子轨迹识别方法，利用成像传感器读取闪烁光纤信号。我们采用变分自编码器（VAE）模型，仅在纯背景帧上训练，实现对含有粒子轨迹信号帧的高效过滤与识别。该方法在实验数据上验证了出色性能，能快速区分信号与背景噪声，具备作为硬件端实时异常检测工具的潜力。研究展示了先进传感器技术与机器学习结合在粒子探测与追踪中的巨大前景。
原文摘要 · Abstract (English)
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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