Connecting Physics Models and Diagnostic Data using
Bayesian Graphical Models
J. Svensson, O. Ford, A. Werner, G. von Nessi, M. Hole, D.C. McDonald,
L. Appel, M. Beurskens, A. Boboc, M. Brix, J. Howard, B.D. Blackwell,
J. Bertram, D. Pretty and JET EFDA contributors
With increasingly detailed physics questions to ask, and with more advanced diagnostics
available, there is a strong case for trying to generalise the way analysis of diagnostic data, and
connection to underlying physics models, is done in today's experiments. With current analysis chains, it
is difficult, verging on impossible, to fully grasp the exact assumptions, hidden in different
legacy codes, that goes into a full analysis of the main physics parameters in an experiment. We
show that by using Bayesian probability theory as the underlying inference method, it is possible
to generalise scientific analysis itself, and therefore build an effective and modular
scientific inference software infrastructure. The Minerva framework uses the concept of Bayesian
graphical models to model the full set of dependencies, functional and probabilistic, between
physics assumptions and diagnostic raw data. Using a graph structure, large scale inference systems
can be modularly built that optimally and automatically use data from multiple sensors.
The framework, used at the JET, MAST, H1 and W7-X experiments, is exemplified by a number
of JET applications, ranging from inference on the flux surface topology to profile inversions
from multiple diagnostic systems.'
Preprint of Paper to be submitted for publication in Proceedings of the
37th EPS Conference on Plasma Physics, Dublin, Ireland.
(21st June 2010 - 25th June 2010)