Distinguishing Cause from Correlation in Tokamak Experiments to Trigger Edge Localised Plasma Instabilities

TL;DR

This paper develops a Bayesian framework to quantify the success of triggering edge-localised plasma instabilities in fusion experiments, enabling rigorous assessment of whether such events are statistically or deterministically triggered.

Contribution

It introduces a Bayesian approach to accurately estimate ELM triggering probabilities and establish reliable thresholds, advancing control techniques in fusion plasma experiments.

Findings

Bayesian methods effectively quantify ELM trigger probabilities.

Experimental data supports the distinction between statistical and deterministic triggering.

Thresholds for reliable ELM triggering are established.

Abstract

The generic question is considered: How can we determine the probability of an otherwise quasirandom event, having been triggered by an external influence? A specific problem is the quantification of the success of techniques to trigger, and hence control, edge-localised plasma instabilities (ELMs) in magnetically confined fusion (MCF) experiments. The development of such techniques is essential to ensure tolerable heat loads on components in large MCF fusion devices, and is necessary for their development into economically successful power plants. Bayesian probability theory is used to rigorously formulate the problem and to provide a formal solution. Accurate but pragmatic methods are developed to estimate triggering probabilities, and are illustrated with experimental data. These allow results from experiments to be quantitatively assessed, and rigorously quantified conclusions to be formed. Example applications include assessing whether triggering of ELMs is a statistical or deterministic process, and the establishment of thresholds to ensure that ELMs are reliably triggered.