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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, Max Austin, Mike van Zeeland, Azarakhsh Jalalvand, Egemen Kolemen

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.

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

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.
Paper Structure (17 sections, 14 equations, 14 figures, 1 table)

This paper contains 17 sections, 14 equations, 14 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Defining a general taxonomy of signals
  4. Time-Frequency Transformation
  5. Separating Broadband Observations
  6. Separating Coherent Signals from Stochastic Noise
  7. Coherent and Transient Thresholding
  8. Detection Refinement
  9. Surrogate Model
  10. Model Architecture
  11. Results
  12. Investigating DIIID Spectrograms
  13. Benchmarking on TJ-II Spectrograms
  14. Benchmarking on Non-Fusion Datasets
  15. Performance
  16. ...and 2 more sections

Figures (14)

  • Figure 1: Signal taxonomy with example modes and spectra.
  • Figure 2: Signal processing pipeline for ece shot 178631 demonstrating progressive separation of coherent modes from broadband background and stochastic noise. (left) Raw stft power spectra. (center) Welch periodogram, which is the time averaged spectrogram. (right) Thresholding on power spectra.
  • Figure 3: (left) Averaging signals can introduce a bias that removes information about each individual signal. (right) Reconstructing each channel with the information from all other channels leads to a better estimate of the true signal.
  • Figure 4: Blind-spot denoising example with AT-BSN, a more efficient form of AP-BSN. (top) Original spectra with small kernel. (bottom) denoised spectra with large kernel. Although noise is reduced in both cases, the small kernel method still retains largely observable measurements of noise, while the large kernel method removes fluctuations especially at the top.
  • Figure 5: (Top) Original denoised spectra. (Middle) CDF with threshold set at corner. (Bottom) Thresholded spectra.
  • ...and 9 more figures