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Spectral Analysis of Decimetric Solar Bursts Variability

Spectral Analysis of Decimetric Solar Bursts Variability. R. R. Rosa 2 , F. C. R. Fernandes 1 , M. J. A. Bolzan 1 , H. S. Sawant 3 and M. Karlický 4 1 Instituto de Pesquisa e Desenvolvimento (IP&D) Universidade do Vale do Paraíba (UNIVAP) São José dos Campos, SP, Brazil

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Spectral Analysis of Decimetric Solar Bursts Variability

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  1. Spectral Analysis of Decimetric Solar Bursts Variability R. R. Rosa2, F. C. R. Fernandes1, M. J. A. Bolzan1, H. S. Sawant3 and M. Karlický4 1Instituto de Pesquisa e Desenvolvimento (IP&D) Universidade do Vale do Paraíba (UNIVAP) São José dos Campos, SP, Brazil 2Laboratório Associado de Computação e Matemática Aplicada (LAC) 3Divisão de Astrofísica Instituto Nacional de Pesquisas Espaciais (INPE) São José dos Campos, SP, Brazil reinaldo.rosa@pq.cnpq.br 4Astronomical Institute Academy of Sciences of the Czech Republic Ondrejov, Czech Republic

  2. Outline • Decimetric Solar Bursts (DSB) • DSB Spectral Analysis • Classifying Variability Pattern Using the Var[C(L)] and H • A Case Study for Space Weather • Concluding Remarks

  3. Brazilian Solar Spectroscope (BSS) (INPE-São José dos Campos) • 1-2.5 GHz, 3MHz, 3ms, 2-3 s.f.u, 100 channels, 11:00-19:00 UT • http://www.das.inpe.br/fmi/intranet/news.php • Ondrejov Radio Observatory (Czech Republic) • 3GHz, 10ms, 4MHz • http://www.asu.cas.cz/~radio/ Decimetric Solar Bursts Data (Time Series):

  4. SFU Starting 17:13:51.48 UT 25/9/2001 1.6-2.0 GHz

  5. SFU SFU June 06 2000 16:34:00 UT 3GHz

  6. M. Karlický et al. • A&A 375, 638-642 (2001) • Rosa et al. • Adv Space Res 42 844–851(2008) -2 -1.92 logP(w) Log f Non-homogeneous scaling ptocess Previous Results from Spectral Analysis (Power Spectra) Power spectra: 1/f with 1.8 <   2 Complex scaling dynamics (hybrid components: plasma turbulence)   10% α=2(1-H) (Mandelbrot, 1985) H  αH

  7. Non-homogeneous Stochastic Process H and C(L) -1.92 C(L)  L- H = 1-(/2) Peitgen, Jurgen & Saupe Chaos and Fractals, Springer 1993 Var[C(L)] = (1/N)i (Ci - C)2 C(L) is the Auto-correlation function=> Non-stationary intermittent process Problem: Bias in  > 10% Estimating a more robust H … H : “Holder exponent” Non-homogeneous scaling function w(1/L)kH

  8. (H) N=1024 Dynamical Process Var(C)(5%) H White Noise 0.002 0.5 1/f2 0.089 0.6 1/f1.66 0.034 0.8 Lorenz 0.025 0.4 Multip0.0201.1 H : Wavelet Transform Modulus Maxima (WTMM) Halsey et al., PRA 33:1141, 1986; Arneodo et al; Physica A 213:232, 1995. “Singularity Spectrum” where αH (t0) is the Holder exponent (or singularity strength). Characterizes  Non-homogeneous multi-scaling process: p-Model p-Model: 1<H(L)<3

  9. N=L=1024 1.6GHz 2GHz 3GHz Dynamical Process Var(C)(5%) H (1%) White Noise 0.002 0.50 1/f2 0.089 0.60 1/f1.66 0.034 0.80 Lorenz 0.025 0.40 Multip0.0201.10 pModel 0.0151.20 1.6 GHz 0.0121.22 2.0 GHz 0.0101.22 3.0 GHz 0.079 1.22 1.6GHz and 2.0 GHz (6 TS) 3 GHz (1 TS)

  10. A Case Study for Space Weather Solar Flares are classified by their x-ray flux in the 1.0 - 8.0 Angstrom band as measured by the NOAA GOES-8 satellite. On June 6, 2000, two solar flares from active region 9026 registered as powerful X-class eruptions. http://science.nasa.gov/headlines/y2000/ast07jun_1m.htm

  11. June 6, 2000 solar flares (X2.3) 15:00-16:35 UT (NOAA AR 9026)

  12. Var[C(L)] = (1/N)i (Ci - C)2 1min before the flare

  13. Concluding Remarks: • This advanced spectral analysis suggested the influence of both, nonlinear oscillations in the magnetic field (A) + turbulent interaction between electron beams and evaporation shocks (B), on the decimetric radio emission energy source (turbulent non-homogeneous MHD p-model cascade) LAC - CTE http://epacis.orgwww.lac.inpe.br • The results suggest Var[C( L)] or Var[C] x H • as a new metric for Solar radio flux monitoring • VLADA (Virtual Lab for Advanced Data Analysis) (EMBRACE) Thank you for your attention.

  14. Time Series Analysis (High sensitivity): Gradient Pattern Analysis =16 V=9 g=9 =20 “Asymmetry Coefficient”: G= ( - g)/g and Limg G=2 Assireu et al, Physica D 168(1):397, 2002.

  15. G=1.82 G=1.87

  16. Escala local Escala global G= ( - g)/g G(ℓ)

  17. There are 16 time series with 1024 points – square matrices 32x32 The signal is decompose by Daubechies Discrete Wavelet (Db8) (see an example for 512 points) Gradient Spectra G(f)

  18. G =  1/N  [Gi (ℓ)-G(ℓ] Gradient Spectra for Turbulent-like Short Time Series

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