  An integrated data analysis system based on Bayesian inference has been
developed for application to data from multiple diagnostics over the
two-dimensional cross-section of tokamak divertors. Tests of the divertor
multi-instrument Bayesian analysis system (D-MIBAS) on a synthetic data set
(including realistic experimental uncertainties) generated from SOLPS-ITER
predictions of the MAST-U divertor have been performed. The resulting inference
was within 6\%, 5\% and 30\% median absolute percentage error of the
SOLPS-predicted electron temperature, electron density and neutral atomic
hydrogen density, respectively, across a two-dimensional poloidal cross-section
of the MAST-U Super-X outer divertor.
  To accommodate molecular contributions to Balmer emission, an advanced
emission model has been developed which is shown to be crucial for inference
accuracy. Our D-MIBAS system utilises a mesh aligned to poloidal magnetic
flux-surfaces, throughout the divertor, with plasma parameters assigned to each
mesh vertex and collectively considered in the inference. This allowed
comprehensive forward models to multiple diagnostics and the inclusion of
expected physics. This is shown to be important for inference precision when
including molecular contributions to Balmer emission. These developments pave
the way for accurate two-dimensional electron temperature, electron density and
neutral atomic hydrogen density inferences for MAST-U divertor experimental
data for the first time.
