Automated Outlier-Robust Bayesian Profile Fitting for Magnetically Confined Plasmas with Modified Tanh Profiles and Good-and-Bad Gaussian Mixture Likelihoods

Abstract

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 \(n_e\), \(T_e\), \(T_i\), and \(v_T\) 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.