TokEye: Fast Signal Extraction for Fluctuating Time Series via Offline Self-Supervised Learning From Fusion Diagnostics to Bioacoustics

Nathaniel Chen, Kouroche Bouchiat, Peter Steiner, Andrew Rothstein, David Smith +4 more
2/23/2026
eess.SPcs.AIphysics.plasm-ph

Official code: PlasmaControl/tokeye (10 stars, MIT, last commit 2026-02-26).

Abstract

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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Code Implementations(1)

PlasmaControl/tokeyeOfficial100%
MIT

TokEye is a open-source Python-based application for automatic classification and localization of fluctuating signals. It is designed to be used in the context of plasma physics, but can be used for any type of fluctuating signal.

100Shell, PythonNov 16, 20257 months agoMIT
machine-learningspectral-analysistime-series

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Cite this paper

@article{chen2026tokeye,
  title  = {TokEye: Fast Signal Extraction for Fluctuating Time Series via Offline Self-Supervised Learning From Fusion Diagnostics to Bioacoustics},
  author = {Nathaniel Chen and Kouroche Bouchiat and Peter Steiner and Andrew Rothstein and David Smith and Max Austin and Mike van Zeeland and Azarakhsh Jalalvand and Egemen Kolemen},
  year   = {2026},
  eprint = {2602.20317},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.20317v2}
}

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