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Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian Manifolds
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
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Number of Authors: 12262017 (English)In: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 19, no 10, article id 569Article in journal (Refereed) Published
Abstract [en]

Modern experiments in Magnetic Confinement Nuclear Fusion can produce Gigabytes of data, mainly in form of time series. The acquired signals, composing massive databases, are typically affected by significant levels of noise. The interpretation of the time series can therefore become quite involved, particularly when tenuous causal relations have to be investigated. In the last years, synchronization experiments, to control potentially dangerous instabilities, have become a subject of intensive research. Their interpretation requires quite delicate causality analysis. In this paper, the approach of Information Geometry is applied to the problem of assessing the effectiveness of synchronization experiments on JET (Joint European Torus). In particular, the use of the Geodesic Distance on Gaussian Manifolds is shown to improve the results of advanced techniques such as Recurrent Plots and Complex Networks, when the noise level is not negligible. In cases affected by particularly high levels of noise, compromising the traditional treatments, the use of the Geodesic Distance on Gaussian Manifolds allows deriving quite encouraging results. In addition to consolidating conclusions previously quite uncertain, it has been demonstrated that the proposed approach permit to successfully analyze signals of discharges which were otherwise unusable, therefore salvaging the interpretation of those experiments.

Place, publisher, year, edition, pages
MDPI , 2017. Vol. 19, no 10, article id 569
Keywords [en]
information geometry, Geodesic Distance on Gaussian Manifolds, recurrence plots, complex networks, ELMs, sawteeth, pacing experiments, Tokamaks, entropy
National Category
Fusion, Plasma and Space Physics
Identifiers
URN: urn:nbn:se:uu:diva-399660DOI: 10.3390/e19100569ISI: 000414845100063OAI: oai:DiVA.org:uu-399660DiVA, id: diva2:1379679
Note

For complete list of authors see http://dx.doi.org/10.3390/e19100569

Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2019-12-17Bibliographically approved

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Andersson Sundén, ErikBinda, FedericoCecconello, MarcoConroy, SeanDzysiuk, NataliiaEricsson, GöranEriksson, JacobHellesen, CarlHjalmarsson, AndersPossnert, GöranSjöstrand, HenrikSkiba, MateuszWeiszflog, Matthias
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