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  Investigation of complexity dynamics in a DC glow discharge magnetized plasma using recurrence quantification analysis

Mitra, V., Sarma, B., Sarma, A., Janaki, M. S., Sekar Iyengar, A. N., Marwan, N., Kurths, J. (2016): Investigation of complexity dynamics in a DC glow discharge magnetized plasma using recurrence quantification analysis. - Physics of Plasmas, 23, 6, 062312.
https://doi.org/10.1063/1.4953903

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Mitra, V.1, Author
Sarma, B.1, Author
Sarma, A.1, Author
Janaki, M. S.1, Author
Sekar Iyengar, A. N.1, Author
Marwan, Norbert2, Author              
Kurths, Jürgen2, Author              
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Recurrence is an ubiquitous feature which provides deep insights into the dynamics of real dynamical systems. A suitable tool for investigating recurrences is recurrence quantification analysis (RQA). It allows, e.g., the detection of regime transitions with respect to varying control parameters. We investigate the complexity of different coexisting nonlinear dynamical regimes of the plasma floating potential fluctuations at different magnetic fields and discharge voltages by using recurrence quantification variables, in particular, DET, Lmax, and Entropy. The recurrence analysis reveals that the predictability of the system strongly depends on discharge voltage. Furthermore, the persistent behaviour of the plasma time series is characterized by the Detrended fluctuation analysis technique to explore the complexity in terms of long range correlation. The enhancement of the discharge voltage at constant magnetic field increases the nonlinear correlations; hence, the complexity of the system decreases, which corroborates the RQA analysis.

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 Dates: 2016
 Publication Status: Finally published
 Pages: -
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 Rev. Type: Peer
 Identifiers: DOI: 10.1063/1.4953903
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 7368
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
Organisational keyword: RD4 - Complexity Science
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
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Title: Physics of Plasmas
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 23 (6) Sequence Number: 062312 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1603092