2026/07/13 by Jaewook Kim, Jekil Lee, Laurent Jung +2
#physics.plasm-ph
We present an outlier-robust Bayesian approach for automated kinetic profile fitting in magnetically confined plasmas with the modified tanh (mtanh) parametrisation and demonstrate its implementation on KSTAR. The method addresses two systematic obstacles: anomalous diagnostic channels can bias least-squares fits, and multimodality of the mtanh cost surface can trap deterministic optimisers in secondary minima. The deployed workflow uses a good-and-bad Gaussian mixture likelihood based on the Box--Tiao formulation as the default outlier-robust likelihood for fitted diagnostic channels, with posterior outlier probabilities retained as channel-level quality indicators. The posterior is sampled with an affine-invariant ensemble MCMC sampler initialised near the result of deterministic maximum a posteriori (MAP)-seeking optimisation, reducing sensitivity to secondary minima on the multimodal mtanh surface. A batch automation layer retrieves diagnostic data from MDSplus and fits arbitrary time slices in parallel for the quantities \(ne\), \(Te\), \(Ti\), and \(vT\) for which the relevant diagnostics are available. Results are written in formats suitable for MDSplus upload and downstream analysis. Representative KSTAR H-mode cases show that the mixture likelihood downweights contaminated measurements while preserving plausible pedestal profiles. The workflow provides a practical basis for future large-scale kinetic profile production for kinetic-EFIT, TRANSP, FASTRAN, and data-driven analysis workflows.