用自监督学习从高噪声数据中快速提取等离子体波动模式。
TokEye: Fast Signal Extraction for Fluctuating Time Series via Offline Self-Supervised Learning From Fusion Diagnostics to Bioacoustics
- 通过多通道非线性优化与神经网络代理，实现信号模式自动提取。
- 在DIII-D等平台验证，推理延迟仅0.5秒，支持实时分析。
- 适用于核聚变、生物声学等多领域波动信号处理，适合控制研究者使用。
下一代核聚变装置如ITER每日产生拍字节级的多诊断信号，超出人工分析能力。本文提出一种“信号优先”的自监督框架，可从高噪声的时间-频率数据中自动提取相干与瞬态模式。该方法结合多通道非线性最优信号处理技术，采用快速神经网络代理，对DIII-D的快速磁探针、电子回旋辐射、CO2干涉仪及束发射光谱数据进行分析。在DIII-D、TJ-II及非聚变光谱图上验证结果有效。推理延迟仅为0.5秒，支持实时模式识别与大规模自动化数据库构建，助力先进等离子体控制。代码开源：https://github.com/PlasmaControl/TokEye。
原文摘要 · Abstract (English)
Next-generation fusion facilities like ITER face a "data deluge," generating petabytes of multi-diagnostic signals daily that challenge manual analysis. We present a "signals-first" self-supervised framework for the automated extraction of coherent and transient modes from high-noise time-frequency data across a variety of sensors. We also develop a general-purpose method and tool for extracting coherent, quasi-coherent, and transient modes for fluctuation measurements in tokamaks by employing non-linear optimal techniques in multichannel signal processing with a fast neural network surrogate on fast magnetics, electron cyclotron emission, CO2 interferometers, and beam emission spectroscopy measurements from DIII-D. Results are tested on data from DIII-D, TJ-II, and non-fusion spectrograms. With an inference latency of 0.5 seconds, this framework enables real-time mode identification and large-scale automated database generation for advanced plasma control. Repository is in https://github.com/PlasmaControl/TokEye.
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