Many important phenomena in fusion plasmas exhibit a strongly stochastic character, i.e. they behave in a random and unpredictable way due to the complex underlying microscopic physics. Examples are edge-localized modes (ELMs), disruptions and plasma turbulence, all of which are presently insufficiently understood from the physical point of view, while posing many critical issues on the road towards a practical fusion reactor. Understanding physical behavior begins with quantification and to quantify a stochastic phenomenon requires the methods of probability theory. We use a variety of probability distributions to model stochastic plasma quantities, in combination with a cutting-edge mathematical framework to describe and compare distributions. This is the framework of information geometry, wherein probability distributions are treated as points on a manifold, allowing measurement of the difference between distributions by means of a geodesic distance. This research field is an exciting blend of plasma physics, probability theory and differential geometry, offering unparalleled new insights into the complex physics of fusion plasmas and in information theory.
An ELM event in MAST recording with a fast camera, showing plasma filaments aligned with the magnetic field.