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.

Murari, A., Craciunescu, T., Peluso, E., Gelfusa, M. (2017). Detection of causal relations in time series affected by noise in tokamaks using geodesic distance on gaussian manifolds. ENTROPY, 19(10), 569 [10.3390/e19100569].

Detection of causal relations in time series affected by noise in tokamaks using geodesic distance on gaussian manifolds

Peluso E.;Gelfusa M.
2017-01-01

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.
2017
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-IND/18 - FISICA DEI REATTORI NUCLEARI
English
information geometry; Geodesic Distance on Gaussian Manifolds; recurrence plots; complex networks; ELMs; sawteeth; pacing experiments; Tokamaks; entropy
Murari, A., Craciunescu, T., Peluso, E., Gelfusa, M. (2017). Detection of causal relations in time series affected by noise in tokamaks using geodesic distance on gaussian manifolds. ENTROPY, 19(10), 569 [10.3390/e19100569].
Murari, A; Craciunescu, T; Peluso, E; Gelfusa, M
Articolo su rivista
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/240252
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 7
social impact