Bayesian Integrated Estimation of Tungsten Concentration at WEST Using Soft X-Ray Spectroscopy

Inference of plasma conditions from diagnostic measurements can often benefit from an integrated analysis, combining measurements from multiple diagnostics. This can be achieved in a Bayesian probabilistic framework, which allows determining the probability distribution of the physical quantities of interest that is compatible with all available data. The approach, sometimes referred to as integrated data analysis (IDA), therefore takes care of data fusion and error propagation at the same time, incorporating constraints on the inferred quantities by means of prior distributions. This contribution concerns IDA for determining impurity concentrations at WEST, based on measurements from soft X-ray spectroscopy (SXR), combined with measurements from interferometry and electron cyclotron emission. The goal is to reconstruct the tungsten distribution in a poloidal cross-section, using a Gaussian process to model the SXR emissivity profile. Ultimately, the aim is to use these results for impurity control, so the inference process will need to be approximated or emulated using appropriately fast models (e.g. neural networks). In this contribution we show first results of reconstructed SXR emissivity profiles from synthetic data, as well as an illustration of Bayesian integrated estimation of tungsten concentration in the plasma.