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Nuclear Data Evaluation with Bayesian Networks

Nuclear Data Evaluation with Bayesian Networks

Presented at the 10th Annual BayesiaLab Conference on Thursday, October 27, 2022.

Abstract

Nuclear data evaluation is concerned with the collection and joint uncertainty quantification of data from nuclear physics experiments with the goal of producing precise estimates of nuclear quantities that can be used in applications ranging from astrophysics over nuclear medicine to nuclear energy. Data stemming from different experiments are often not directly comparable due to experimental aspects, such as the finite energy resolution of detectors, but are nevertheless related to each other as they link back to the same fundamental nuclear quantities. The Bayesian network framework is particularly well suited to model these relationships and bears the promise to accelerate the production of high-quality nuclear data evaluations in the future and to facilitate the consideration of physical constraints that are often not explicitly modeled.

Presentation Video

Presentation Slides

About the Presenter

Dr. Georg Schnabel works in the Nuclear Data Section in the Division of Physical and Chemical Sciences of the International Atomic Energy Agency. His responsibilities include the development of scientific codes for data analysis and management and the organization and coordination of technical meetings and workshops on topics ranging from nuclear data libraries to machine learning with the objective of improving the availability, comprehensiveness, and quality of nuclear data. Prior to working at the IAEA and after graduating from the Technical University of Vienna in Austria, Dr. Schnabel was working as a researcher at the University of Uppsala in Sweden and the French Alternative Energies and Atomic Energy Commission (CEA) specializing in the development and application of Bayesian methods for uncertainty quantification in the domain of nuclear physics.


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