Comparative blobs and holes dynamics in a tokamak plasma: deep learning analysis of fast imaging data

TL;DR

This study uses deep learning to analyze the dynamics of blobs and holes in tokamak plasma turbulence, revealing that many negative structures are artifacts and proposing a method to better identify true holes.

Contribution

The paper introduces a deep learning approach to distinguish genuine plasma holes from artifacts in fast imaging data, improving understanding of their dynamics.

Findings

Negative structures are mostly artifacts from data pre-processing.

Retaining only supernumerary negative structures aligns their behavior with expected holes.

Deep learning helps differentiate real plasma structures from artifacts.

Abstract

Abstract This work focuses on the dynamics of the turbulent structures revealed by tomographic inversion of fast passive imaging data acquired on the COMPASS tokamak. To highlight the fluctuations, a sliding median image is subtracted from each image, revealing positive and negative structures. Assuming that the positive structures are blobs and the negative structures are holes, a recently developed deep learning analysis method is used to compare the dynamics of the two types of structures. While the results obtained for the positive structures seem to be in line with the dynamics expected for blobs, contradictory results are obtained for the negative structures, since their dynamics are very similar to those of blobs whereas they should be opposite. Our work suggests that the majority of negative structures resulting from data pre-processing are artefacts produced by the latter. However, a basic approach that only retains supernumerary negative structures shows that the behaviour of the latter is consistent with that expected for holes, opening new perspectives for their investigation.