An important step in the quest for low-cost fusion power is the ability to perform and analyze experiments in prototype fusion reactors. One of the tasks in the analysis of experimental data is the classification of orbits in Poincare plots. These plots are generated by the particles in a fusion reactor as they move within the toroidal device. In this paper, we describe the use of graph-based methods to extract features from orbits. These features are then used to classify the orbits into several categories. Our results show that existing machine learning algorithms are successful in classifying orbits with few …
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An important step in the quest for low-cost fusion power is the ability to perform and analyze experiments in prototype fusion reactors. One of the tasks in the analysis of experimental data is the classification of orbits in Poincare plots. These plots are generated by the particles in a fusion reactor as they move within the toroidal device. In this paper, we describe the use of graph-based methods to extract features from orbits. These features are then used to classify the orbits into several categories. Our results show that existing machine learning algorithms are successful in classifying orbits with few points, a situation which can arise in data from experiments.
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Bagherjeiran, A. & Kamath, C.Graph-based Methods for Orbit Classification,
article,
September 29, 2005;
Livermore, California.
(https://digital.library.unt.edu/ark:/67531/metadc891512/:
accessed May 10, 2024),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT Libraries Government Documents Department.
