Evidence cross-validation and Bayesian inference of MAST plasma equilibria

@article{Nessi2012EvidenceCA,
  title={Evidence cross-validation and Bayesian inference of MAST plasma equilibria},
  author={Gregory Von Nessi and Matthew J. Hole and Jakob Svensson and L. C. Appel},
  journal={Physics of Plasmas},
  year={2012},
  volume={19},
  pages={012506}
}
This work was jointly funded by the Australian Government through International Science Linkages Grant No. CG130047, the Australian National University, the United Kingdom Engineering and Physical Sciences Research Council under Grant No. EP/G003955, and by the European Communities under the contract of Association between EURATOM and CCFE. 

Figures from this paper

A unified method for inference of tokamak equilibria and validation of force-balance models based on Bayesian analysis

A new method, based on Bayesian analysis, is presented which unifies the inference of plasma equilibria parameters in a tokamak with the ability to quantify differences between inferred equilibria

An equilibrium validation technique based on Bayesian inference

In recent years, Bayesian probability theory has been used in a number of experiments to fold uncertainties and interdependences in the diagnostic data and forward models, and together with prior

Development and simulation of multi-diagnostic Bayesian analysis for 2D inference of divertor plasma characteristics

We present results of the design, implementation and testing of a Bayesian multi-diagnostic inference system which combines various divertor diagnostics to infer the 2D fields of electron temperature

Recent developments in Bayesian inference of tokamak plasma equilibria and high-dimensional stochastic quadratures

TLDR
State-of-the-art results of using BEAST to study MAST equilbria are reviewed, with recent code advancements being systematically presented though out the manuscript.

Bayesian approach for validation of runaway electron simulations

TLDR
This paper implements this type of Bayesian optimization framework for a model for analysis of disruption runaway electrons, and uses this proof-of-principle framework to explore the optimum input parameters with uncertainties in optimization tasks ranging from one to seven dimensions.

Plasma parameter profile inference from limited data utilizing second-order derivative priors and physic-based constraints

TLDR
A new profile inference framework is proposed that utilizes prior knowledge about plasma physics, along with integrated data analysis and a Gaussian process to facilitate the use of the Markov chain Monte Carlo sampling and define quantities corresponding to the second derivatives of the profiles.

Bayesian modelling of the emission spectrum of the Joint European Torus Lithium Beam Emission Spectroscopy system.

TLDR
The proposed approach makes it possible to extract the intensity of Li line without doing a separate background subtraction through modulation of the Li beam using a Bayesian linear inversion technique.

Plasma profile tomography for EAST based on integrated data analysis

TLDR
A plasma profile reconstruction algorithm based on integrated data analysis (IDA) is proposed, which incorporates various diagnostics and can provide two-dimensional distributions of plasma current and electron density and improves the current distribution in the core and increases the accuracy of plasma profiles reconstruction.

Experimentally testing the dependence of momentum transport on second derivatives using Gaussian process regression

It remains an open question to explain the dramatic change in intrinsic rotation induced by slight changes in electron density (White et al 2013 Phys. Plasmas 20 056106). One proposed explanation is

References

SHOWING 1-10 OF 23 REFERENCES

Large Scale Bayesian Data Analysis for Nuclear Fusion Experiments

TLDR
This paper will outline and exemplify how this method is changing data analysis in nuclear fusion and discuss architectural issues relating to the very complex analysis systems that might emerge from a systematic application of this method in large scientific experiments.

Genetic algorithms in plasma diagnostic analysis

A novel sophisticated technique for data reduction utilising Bayesian statistics and a genetic algorithm has been developed by the authors. The technique gives superior signal recovery in poor

Integrating diagnostic data analysis for W7-AS using Bayesian graphical models

TLDR
An integrated data analysis model is demonstrated, applied to the W7-AS stellarator, where diagnostic interdependencies have been modeled in a novel way by using so called Bayesian graphical models.

Topics and Methods for Data Validation by Means of Bayesian Probability Theory

Abstract Steady-state fusion devices, such as Wendelstein 7-X, require new approaches for data analysis. These efforts are motivated by both the physics and the technical requirements of steady-state

A Unified Approach to Equilibrium Reconstruction

UKAEA/EURATOM Fusion Association, Culham Science Centre, Abingdon Oxon OX14 3DB; Association Euratom-CEA, CEA/DSM/DRFC, Centre d Cadarache 13108 Saint Paul lez Durance, France; General Atomics, San

Forward modeling of JET polarimetry diagnostic.

TLDR
An analytical Bayesian inversion of the JET interferometry line integrated densities into density profiles and associated uncertainty information, is demonstrated, and good agreement with measured values is shown for a number of channels.

Data analysis : a Bayesian tutorial

TLDR
This tutorial jumps right in to the power ofparameter estimation without dragging you through the basic concepts of parameter estimation.

Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support

TLDR
The how-to of Bayesian inference is explained and a model fitting guide is given for linear model fitting and nonlinear model fitting of the Markov Chain Monte Carlo model.

Current tomography for axisymmetric plasmas

A novel method for inferring the toroidal current distribution from measurements of magnetic field and flux is presented. The method uses a Bayesian approach that gives the full joint probability

Bayesian Data Analysis

TLDR
Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.