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Edge localized modes (ELMs) in tokamak plasmas can be detected using Doppler backscattering (DBS) data. Neural networks trained on DBS data achieve high accuracy, offering a robust method for ELM detection in future fusion reactors.

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Area of Science:

  • Plasma physics
  • Fusion energy research
  • Machine learning applications

Background:

  • Edge localized modes (ELMs) are plasma ejections in H-mode tokamak operation.
  • ELMs cause energy loss and vessel damage, necessitating effective detection and mitigation strategies.
  • Current diagnostic methods like deuterium-alpha (Dα) spectroscopy have limitations.

Purpose of the Study:

  • To develop and evaluate a neural network model for detecting ELMs using Doppler backscattering (DBS) data.
  • To establish DBS as a viable diagnostic for ELM detection in future operational tokamaks.
  • To demonstrate the broader applicability of neural networks to DBS diagnostic data.

Main Methods:

  • Utilized the DIII-D tokamak database for training and testing.
  • Trained a neural network to classify time steps based on ELM events using Dα data as ground truth.
  • Evaluated model performance across various ELM types and confinement regimes.

Main Results:

  • Achieved a high f1-score of 0.93 on test data, indicating robust ELM detection.
  • Demonstrated consistent performance across different ELM regimes (grassy, RMP mitigated, wide-pedestal).
  • Confirmed the broad applicability of neural networks for analyzing DBS diagnostic data.

Conclusions:

  • Neural networks provide a promising and accurate method for ELM detection using DBS data.
  • DBS, with its high temporal resolution and robustness, is a valuable diagnostic for future tokamaks.
  • This work validates the use of machine learning for processing DBS data, extending beyond ELM detection.