Integrated Data Analysis and Validation

TL;DR

This paper discusses the development of an integrated data analysis framework for nuclear fusion diagnostics, leveraging Bayesian probability to combine heterogeneous data sources for improved accuracy and consistency.

Contribution

It introduces a probabilistic, Bayesian-based approach to integrate diverse diagnostic data, enhancing analysis reliability and resolving inconsistencies in fusion research.

Findings

Probabilistic framework improves data consistency.

Enhanced parameter estimation accuracy.

Better resolution of diagnostic interdependencies.

Abstract

A major challenge in nuclear fusion research is the coherent combination of data from heterogeneous diagnostics and modelling codes for machine control and safety as well as physics studies. Measured data from different diagnostics often provide information about the same subset of physical parameters. Additionally, information provided by some diagnostics might be needed for the analysis of other diagnostics. A joint analysis of complementary and redundant data allows, e.g., to improve the reliability of parameter estimation, to increase the spatial and temporal resolution of profiles, to obtain synergistic effects, to consider diagnostics interdependencies and to find and resolve data inconsistencies. Physics-based modelling and parameter relationships provide additional information improving the treatment of ill-posed inversion problems. A coherent combination of all kind of available information within a probabilistic framework allows for improved data analysis results. The concept of Integrated Data Analysis (IDA) in the framework of Bayesian probability theory is outlined and contrasted with conventional data analysis. Components of the probabilistic approach are summarized and specific ingredients beneficial for data analysis at fusion devices are discussed.