Recurrence network measures for hypothesis testing using surrogate data: Application to black hole light curves

Jacob, Rinku; Harikrishnan, K. P.; Misra, Ranjeev; Ambika, G.

doi:10.1016/j.cnsns.2017.05.018

Cited by 14 publications

(6 citation statements)

References 41 publications

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“…Recurrence based measures have been applied for the study of black holes (Jacob et al (2018)), variable stars (George et al (2019)), solar radiation (Ogunjo et al (2017)) and exoplanetary systems (Kovács (2019). A rather remarkable advantage of RQA measures is their ability to detect dynamical transitions from a time series even when the details of the underlying dynamics are elusive (Marwan et al (2013); Thiel et al (2004)).…”

Section: Recurrence Based Analysismentioning

confidence: 99%

Early warning signals indicate a critical transition in Betelgeuse

George

Kachhara

Misra

et al. 2020

A&A

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Context. Critical transitions occur in complex dynamical systems when the system dynamics undergoes a regime shift. These can often occur with little change in the mean amplitude of the system response prior to the actual time of transition. The recent dimming and brightening event in Betelgeuse occurred as a sudden shift in the brightness and has been the subject of much debate. Internal changes or an external dust cloud have been suggested as reasons for this change in variability. Aims. We examine whether the dimming and brightening event of 2019–20 could be due to a critical transition in the pulsation dynamics of Betelgeuse by studying the characteristics of the light curve prior to transition. Methods. We calculated the quantifiers hypothesized to rise prior to a critical transition for the light curve of Betelgeuse up to the dimming event of 2019–20. These included the autocorrelation at lag-1, variance, and the spectral coefficient calculated from detrended fluctuation analysis, in addition to two measures that quantify the recurrence properties of the light curve. Significant rises are confirmed using the Mann-Kendall trend test. Results. We see a significant increase in all quantifiers (p < 0.05) prior to the dimming event of 2019–20. This suggests that the event was a critical transition related to the underlying nonlinear dynamics of the star. Conclusions. Together with results that suggest a minimal change in Teff and IR flux, a critical transition in the pulsation dynamics might be a reason for the unprecedented dimming of Betelgeuse. The rise in the quantifiers we studied prior to the dimming event supports this possibility.

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Section: Recurrence Based Analysismentioning

confidence: 99%

Early warning signals indicate a critical transition in Betelgeuse

George

Kachhara

Misra

et al. 2020

A&A

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“…Another limitation of the method as presented in this paper is the lack of a reference for determining whether a peak is a potential EWS or merely a random fluctuation. There are several ways to at least check whether the observed peaks are “real”, for example, by repeating the analysis several times on surrogate data (e.g., Jacob et al, 2018 ) or perform a block-randomization on the windowed time course (e.g., Vink et al, 2018 ). Another way to construct a randomization test could be by randomly re-wiring the network layers and re-construct the Multiplex network and evaluate and compare the layer similarities to the observed measures.…”

Section: Discussionmentioning

confidence: 99%

Early Warning Signals in Phase Space: Geometric Resilience Loss Indicators From Multiplex Cumulative Recurrence Networks

Hasselman

2022

Front. Physiol.

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The detection of Early Warning Signals (EWS) of imminent phase transitions, such as sudden changes in symptom severity could be an important innovation in the treatment or prevention of disease or psychopathology. Recurrence-based analyses are known for their ability to detect differences in behavioral modes and order transitions in extremely noisy data. As a proof of principle, the present paper provides an example of a recurrence network based analysis strategy which can be implemented in a clinical setting in which data from an individual is continuously monitored for the purpose of making decisions about diagnosis and intervention. Specifically, it is demonstrated that measures based on the geometry of the phase space can serve as Early Warning Signals of imminent phase transitions. A publicly available multivariate time series is analyzed using so-called cumulative Recurrence Networks (cRN), which are recurrence networks with edges weighted by recurrence time and directed towards previously observed data points. The results are compared to previous analyses of the same data set, benefits, limitations and future directions of the analysis approach are discussed.

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“…We have shown that [37] this critical range is approximately identical for several chaotic systems and white noise and de-pends only on the embedding dimension M. The threshold values used here for the construction of the network are ǫ = 0.06, 0.10, 0.14 and 0.18 for M varying from 2 to 5 respectively. The scheme has been effectively employed to study the influence of noise on the structure of chaotic attractors [38] and for the analysis of light curves from a prominent black hole system [39]. Here we use this scheme for the construction of RN from time series.…”

Section: Recurrence Network and Entropy Measurementioning

confidence: 99%

Quantifying information loss on chaotic attractors through recurrence networks

2019

Self Cite

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We propose an entropy measure for the analysis of chaotic attractors through recurrence networks which are un-weighted and un-directed complex networks constructed from time series of dynamical systems using specific criteria. We show that the proposed measure converges to a constant value with increase in the number of data points on the attractor (or the number of nodes on the network) and the embedding dimension used for the construction of the network, and clearly distinguishes between the recurrence network from chaotic time series and white noise. Since the measure is characteristic to the network topology, it can be used to quantify the information loss associated with the structural change of a chaotic attractor in terms of the difference in the link density of the corresponding recurrence networks. We also indicate some practical applications of the proposed measure in the recurrence analysis of chaotic attractors as well as the relevance of the proposed measure in the context of the general theory of complex networks.

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Recurrence network measures for hypothesis testing using surrogate data: Application to black hole light curves

Cited by 14 publications

References 41 publications

Early warning signals indicate a critical transition in Betelgeuse

Early warning signals indicate a critical transition in Betelgeuse

Early Warning Signals in Phase Space: Geometric Resilience Loss Indicators From Multiplex Cumulative Recurrence Networks

Quantifying information loss on chaotic attractors through recurrence networks

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