A Bayesian model of filamentary dynamics in MAST

Abstract

Abstract A novel approach using Bayesian inference has been implemented to interpret the filamentary dynamics measured by a Langmuir probe fixed to a reciprocating assembly on MAST. The model describes the system as a superposition of time-displaced filaments and a fixed background component. Each filament is parameterised in terms of a characteristic rise and fall time and maximum amplitude centred on local maxima in the measured data time-series. A distinctive feature of the approach is that no minimum threshold is set for the existence of filaments. It is observed that whereas large amplitude filaments are well characterised in terms of rise times, smaller amplitude filaments are often unconstrained by the data and are limited by the details of the prior. Based on these findings, a new definition for the plasma filaments is proposed based on the uncertainty in the filament rise times. The remaining filaments together with the constant background component forms a new time-dependent signal referred to as the computed background fluctuation signal. The characteristics of these signals (for the plasma filaments and for the background fluctuations) are reported in terms of their spatial variation as the probe moves through the SOL and into the core plasma.