Tracking Blobs in the Turbulent Edge Plasma of a Tokamak Fusion Device

TL;DR

This paper introduces a novel motion tracking approach to identify and analyze turbulent blobs in tokamak plasma videos, validating methods with synthetic and real data, and providing a public dataset to broaden research access.

Contribution

It applies and compares four baseline motion tracking methods to plasma blob detection, and releases a dataset and benchmark to facilitate wider research in plasma turbulence.

Findings

Blob trajectories align with state-of-the-art methods.

Baseline methods effectively identify turbulent regimes.

Public dataset enables broader community engagement.

Abstract

The analysis of turbulence in plasmas is fundamental in fusion research. Despite extensive progress in theoretical modeling in the past 15 years, we still lack a complete and consistent understanding of turbulence in magnetic confinement devices, such as tokamaks. Experimental studies are challenging due to the diverse processes that drive the high-speed dynamics of turbulent phenomena. This work presents a novel application of motion tracking to identify and track turbulent filaments in fusion plasmas, called blobs, in a high-frequency video obtained from Gas Puff Imaging diagnostics. We compare four baseline methods (RAFT, Mask R-CNN, GMA, and Flow Walk) trained on synthetic data and then test on synthetic and real-world data obtained from plasmas in the Tokamak `a Configuration Variable (TCV). The blob regime identified from an analysis of blob trajectories agrees with state-of-the-art conditional averaging methods for each of the baseline methods employed, giving confidence in the accuracy of these techniques. High entry barriers traditionally limit tokamak plasma research to a small community of researchers in the field. By making a dataset and benchmark publicly available, we hope to open the field to a broad community in science and engineering.