Fractality and singularity in CME linear speed signal: Cycle 23

Chattopadhyay, Anirban; Khondekar, Mofazzal H.; Bhattacharjee, Anup Kumar

doi:10.1016/j.chaos.2018.08.008

Cited by 9 publications

(7 citation statements)

References 36 publications

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“…The skewness ( r = max − 0 0 − min ) is also determined for K p -index signal which is 1.1359. There is an inverse relationship between the values of and the strength of the singularity whereas for a rough signal having high magnitude singularities are characterised by low value [46]. For right skewed profile:r > 1 and it signifies the singularities of lower strength are predominant in the signal.…”

Section: Resultsmentioning

confidence: 99%

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Analysis of fractality and complexity of the planetary K-index

Chattopadhyay¹,

Chandra

Khondekar³

et al. 2021

SN Appl. Sci.

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The objective of this research is to explore the inherent complexities and multifractal properties of the underlying distributions in the daily Planetary K-index time series collected from NOAA Space Weather Prediction Center. In this article, non-stationary and nonlinear characteristics of the signal have been explored using Smoothed Pseudo Wigner–Ville Distribution and Delay Vector Variance algorithms, respectively, while Recurrence Plot, 0–1 test, Recurrence Quantification Analysis and correlation dimension analysis have been applied to confirm and measure the chaos in the signal under consideration. Multifractal detrending moving average has been used to evaluate the multifractality and also recognise the singularities of the signal. The result of these analyses validates the nonstationary and nonlinear characteristics of the Planetary K-index signal, while a significant presence of deterministic chaos in it has also been noticed. It has also been confirmed that the Planetary K-index exhibits multifractal nature with positive persistence. The long-range temporal association and also the large pdf are discovered to be the primary factors that contribute to the multifractal behaviour of the Kp-index.

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Section: Resultsmentioning

confidence: 99%

“…fractal systems can be typified by self-similarity. MFDMA can be considered as an effective method for evaluating the fractal characteristics for any nonstationary time series signal [29,46].…”

Section: Mfdmamentioning

confidence: 99%

Analysis of fractality and complexity of the planetary K-index

Chattopadhyay¹,

Chandra

Khondekar³

et al. 2021

SN Appl. Sci.

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“…Additionally, the 𝑝-model was used to reproduce the multifractal behavior of the solar wind series, indicating that a nonlinear turbulence energy cascade dynamical system is behind the observed dynamics. A similar framework for multifractal analysis, but without the volatility and the 𝑝-model, was used by Chattopadhyay et al (2018) in the analysis of CME linear speed data in the solar wind. In order to keep the paper reasonably short, we have limited our presentation to only two time-series, but we have tested our techniques in other series and found that the conclusions presented are robust.…”

Section: Discussionmentioning

confidence: 99%

“…Studies of surrogate time series have been conducted to probe the origin of multifractality in a wide range of contexts, including financial markets (Barunik et al 2012), human gate diseases (Dutta et al 2013), near-fault earthquake ground motions (Yang et al 2015), solar irradiance fluctuations (Madanchi et al 2017), air pollutants (Dong et al 2017), meteorological time series of air pressure, air temperature and wind speed (Gos et al 2021) and rainfall records (Sarker & Mali 2021). The surrogate method was also employed in time series of CME linear speed during solar cycle 23 to conclude that the multifractality is due to both the broad PDF and long range time correlations (Chattopadhyay et al 2018). In the present paper, we use the method to reveal the role of current sheets in the origin of multifractality in the solar wind.…”

Section: Introductionmentioning

confidence: 99%

Origin of multifractality in solar wind turbulence: the role of current sheets

Gomes

Rempel

et al. 2022

Monthly Notices of the Royal Astronomical Society

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In this work, a multifractal framework is proposed to investigate the effects of current sheets in solar wind turbulence. By using multifractal detrended fluctuation analysis coupled with surrogate methods and volatility, two solar wind magnetic field time series are investigated, one with current sheets and one without current sheets. Despite the lack of extreme-events intermittent bursts in the current sheet-free series, both series are shown to be strongly multifractal, although the current sheet-free series displays an almost linear behavior for the scaling exponent of structure functions. Long-range correlations are shown to be the main source of multifractality for the series without current sheets, while a combination of heavy-tail distribution and nonlinear correlations are responsible for multifractality in the series with current sheets. The multifractality in both time series is formally shown to be associated with an energy-cascade process using the p-model.

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“…Assuming

\left\{{r}_i\right\}

and

\left\{{s}_i\right\}

as two time series of length L , where

i=1,\cdots, L

MFXDFA algorithm is summarized as follows based on MFDFA algorithm (Zhou 2008) (Chattopadhyay et al, Fractality and singularity in CME linear speed signal: Cycle 23 Chattopadhyay et al 2018): Step 1: Determine the signal profile of the time series,

R(i)=\sum \limits_{n=1}^i\left(r(n)-\overline{r}\right);\kern1em i=1,\cdots, L

S(i)=\sum \limits_{n=1}^i\left(s(n)-\overline{s}\right);\kern1em i=1,\cdots, L

where

\overline{r}

and

\overline{s}

are the sample averages. Step 2: Profile

R(i)

and

…”

Section: Algorithms For Analysismentioning

confidence: 99%

Multifractal cross‐correlation analysis between CME and Planetary K‐index time series

Chattopadhyay¹,

Khondekar²

2023

Astron Nachr

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This research aims to examine the multiscale‐multifractal correlation properties between the geomagnetic storm and coronal mass ejection (CME) occurrences by analyzing the CME linear speed and Planetary K‐index time series data. The relevant data for both CME and geomagnetic storm occurrences were obtained from the Solar and Heliospheric Observatory mission's LASCO and the NOAA Space Weather Prediction Center, respectively, for the same period (February 1999 to December 2007). We performed MultiFractal cross‐correlation Detrended Fluctuation Analysis (MFXDFA) and Multifractal cross‐correlation Detrending Moving average Analysis (MFXDMA) to investigate and quantify the possible cross‐correlation between the two natural events. The MFXDFA technique is also compared to the backward MFXDMA algorithm's performance. The change in the degree of cross‐correlation over time has been investigated, and the findings are quantitatively analyzed. The existence of significant power‐law cross‐correlations has been discovered within all scaling orders. Furthermore, we also find evident persistence of cross‐correlation with substantial Hurst exponents. In addition, it has been observed that long‐term cross‐correlation has a more considerable degree of multifractality and persistence than short‐term cross‐correlation.

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Fractality and singularity in CME linear speed signal: Cycle 23

Cited by 9 publications

References 36 publications

Analysis of fractality and complexity of the planetary K-index

Analysis of fractality and complexity of the planetary K-index

Origin of multifractality in solar wind turbulence: the role of current sheets

Multifractal cross‐correlation analysis between CME and Planetary K‐index time series

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