Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian Manifolds
Abstract: 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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-10-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/19/10/569 |
| _version_ | 1828112710490914816 |
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| author | Andrea Murari Teddy Craciunescu Emmanuele Peluso Michela Gelfusa JET Contributors |
| author_facet | Andrea Murari Teddy Craciunescu Emmanuele Peluso Michela Gelfusa JET Contributors |
| author_sort | Andrea Murari |
| collection | DOAJ |
| description | Abstract: 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. |
| first_indexed | 2024-04-11T11:54:47Z |
| format | Article |
| id | doaj.art-f4aa3362d6c04dd4b5f827f45ac7ec13 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T11:54:47Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-f4aa3362d6c04dd4b5f827f45ac7ec132022-12-22T04:25:12ZengMDPI AGEntropy1099-43002017-10-01191056910.3390/e19100569e19100569Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian ManifoldsAndrea Murari0Teddy Craciunescu1Emmanuele Peluso2Michela Gelfusa3JET Contributors4EUROfusion Consortium, JET, Culham Science Centre, Abingdon OX14 3DB, UKEUROfusion Consortium, JET, Culham Science Centre, Abingdon OX14 3DB, UKEUROfusion Consortium, JET, Culham Science Centre, Abingdon OX14 3DB, UKEUROfusion Consortium, JET, Culham Science Centre, Abingdon OX14 3DB, UKSee the author list of “Overview of the JET results in support to ITER” by X. Litaudon et al. to be published in Nuclear Fusion Special issue: overview and summary reports from the 26th Fusion Energy Conference (Kyoto, Japan, 17–22 October 2016).Abstract: 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.https://www.mdpi.com/1099-4300/19/10/569information geometrygeodesic distance on Gaussian manifoldsrecurrence plotscomplex networksELMssawteethpacing experimentsTokamaksentropy |
| spellingShingle | Andrea Murari Teddy Craciunescu Emmanuele Peluso Michela Gelfusa JET Contributors Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian Manifolds Entropy information geometry geodesic distance on Gaussian manifolds recurrence plots complex networks ELMs sawteeth pacing experiments Tokamaks entropy |
| title | Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian Manifolds |
| title_full | Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian Manifolds |
| title_fullStr | Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian Manifolds |
| title_full_unstemmed | Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian Manifolds |
| title_short | Detection of Causal Relations in Time Series Affected by Noise in Tokamaks Using Geodesic Distance on Gaussian Manifolds |
| title_sort | detection of causal relations in time series affected by noise in tokamaks using geodesic distance on gaussian manifolds |
| topic | information geometry geodesic distance on Gaussian manifolds recurrence plots complex networks ELMs sawteeth pacing experiments Tokamaks entropy |
| url | https://www.mdpi.com/1099-4300/19/10/569 |
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