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Scaling Analysis of the Flare Index Data from Kandilli Observatory

Published online by Cambridge University Press:  27 November 2018

Soumya Roy ,
Amrita Prasad ,
Susovan Chowdhury ,
Subhash Chandra Panja  and
Sankar Narayan Patra
Soumya Roy*
Affiliation:
Dept. of AEIE, Haldia Institute of Technology, Haldia, India
Amrita Prasad
Affiliation:
Dept. of Mechanical Engg., Jadavpur University, Kolkata, India
Susovan Chowdhury
Affiliation:
Dept. of Instrumentation Science, Jadavpur University, Kolkata, India
Subhash Chandra Panja
Affiliation:
Dept. of Mechanical Engg., Jadavpur University, Kolkata, India
Sankar Narayan Patra*
Affiliation:
Dept. of Instrumentation Science, Jadavpur University, Kolkata, India
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Abstract

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The daily time series Flare Index (FI) data of Northern Hemisphere, Southern Hemisphere and Total Disk for Solar Cycle 21- 23 and 24 up to Dec. 2014 has been pre-processed using a 2nd order exponential smoothing algorithm to remove orthogonal noise. The smoothed data in each case is processed for scaling analysis using Rescaled-Range Analysis as well as Finite Variance Scaling Method in order to search for the Hurst exponent. As the value of H obtained from our analysis lies in between 0 and 1, so it can be said that the signal may behave like Fractional Brownian Motion. Also, it is observed that H is less than 0.5 which indicates the data is anti-persistent in nature and it has a strong negative correlation within the signal. The value of H also indicates the oscillating features of the signal which might have some fundamental periodicities in the Suns atmosphere.

Keywords

Flare Index DataExponential SmoothingRescaled-Range AnalysisFinite Variance Scaling MethodHurst ExponentShort Range Dependent memory
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 13 , Symposium S340: Long-term Datasets for the Understanding of Solar and Stellar Magnetic Cycles , February 2018 , pp. 161 - 162
Copyright
Copyright © International Astronomical Union 2018 

References

Wiegelmann, T., Thalmann, J. K., & Solanki, S. K., 2014, A&ARv, 22, 1–106Google Scholar
Zou, P., Li, Q. X., & Wu, N., 2014, MNRAS, 437, 38–45Google Scholar
Sivakumar, B., 2004, CSF, 19, 441–462Google Scholar
Marwan, N., Romano, M. N., Theil, M., & Kurths, J., 2007, PhR, 438, 237–329Google Scholar
Winters, P. R., 1960, Manag. Sci., 6, 324Google Scholar
Mandelbrot, B. B. & Wallis, J. R., 1969, WRR, 3, 321Google Scholar
Scafetta, N. & Grigolini, P., 2002, PhRvE, 66, 036130Google Scholar