Deep modelling of plasma and neutral fluctuations from gas puff turbulence imaging

TL;DR

This paper introduces a deep learning-based method to diagnose edge turbulence in fusion plasmas by translating helium line brightness measurements into local plasma fluctuations, validated on tokamak data.

Contribution

It develops a novel integrated deep learning framework combining physics models to analyze turbulence from spectroscopic data in fusion devices.

Findings

First 2D time-dependent measurements of plasma and neutral fluctuations in tokamak.

Demonstrates transferability of turbulence diagnostics to various plasma environments.

Reveals shadowing effects in fusion plasma using a single spectral line.

Abstract

The role of turbulence in setting boundary plasma conditions is presently a key uncertainty in projecting to fusion energy reactors. To robustly diagnose edge turbulence, we develop and demonstrate a technique to translate brightness measurements of HeI line radiation into local plasma fluctuations via a novel integrated deep learning framework that combines neutral transport physics and collisional radiative theory for the $3^3 D - 2^3 P$ transition in atomic helium. The tenets for experimental validity are reviewed, illustrating that this turbulence analysis for ionized gases is transferable to both magnetized and unmagnetized environments with arbitrary geometries. Based upon fast camera data on the Alcator C-Mod tokamak, we present the first 2-dimensional time-dependent experimental measurements of the turbulent electron density, electron temperature, and neutral density revealing shadowing effects in a fusion plasma using a single spectral line.