spectral analysis of decimetric solar bursts variability
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Spectral Analysis of Spectral Analysis of Decimetric Solar Bursts VariabilityDecimetric 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
4Astronomical InstituteAcademy of Sciences of the Czech Republic
Ondrejov, Czech Republic
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
Decimetric Solar Bursts
Data (Time Series):
•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 UThttp://www.das.inpe.br/fmi/intranet/news.php
•Ondrejov Radio Observatory (Czech Republic) 3GHz, 10ms, 4MHzhttp://www.asu.cas.cz/~radio/
1.6-2.0 GHz
SF
U
Starting 17:13:51.48 UT 25/9/2001
SF
U
June 06 200016:34:00 UT
SF
U
3GHz
Power spectra: 1/f with 1.8 < 2
Complex scaling dynamics(hybrid components: plasma turbulence)
10%
Previous Results from Spectral Analysis (Power Spectra)
•M. Karlický et al. •A&A 375, 638-642 (2001)
-2 -1.92
•Rosa et al.•Adv Space Res 42 844–851(2008)
Log f
log
P(w
)
α=2(1-H) (Mandelbrot, 1985)
H αH
Non-homogeneous scaling ptocess
Non-homogeneous Stochastic Process H and C(L)
Var[C(L)] = (1/N)i (Ci - C)2
Estimating a more robust H …
C(L) L-
H = 1-(/2)
Peitgen, Jurgen & SaupeChaos and Fractals, Springer1993
C(L) is the Auto-correlation function=> Non-stationary intermittent process
Problem: Bias in > 10%
-1.92
H : “Holder exponent”
Non-homogeneous scaling function w(1/L)kH
H : Wavelet Transform Modulus Maxima (WTMM)
“Singularity Spectrum”
(H)
where αH (t0) is the Holder exponent (or singularity strength).
Halsey et al., PRA 33:1141, 1986; Arneodo et al; Physica A 213:232, 1995.
Dynamical Process Var(C)(5%) H
White Noise 0.002 0.51/f2 0.089 0.6 1/f1.66 0.034 0.8Lorenz 0.025 0.4Multip 0.020 1.1
N=1024
p-Model: 1<H(L)<3
Characterizes Non-homogeneous multi-scaling process: p-Model
Dynamical Process Var(C)(5%) H (1%)
White Noise 0.002 0.501/f2 0.089 0.60 1/f1.66 0.034 0.80Lorenz 0.025 0.40Multip 0.020 1.10pModel 0.015 1.20
1.6 GHz 0.012 1.222.0 GHz 0.010 1.223.0 GHz 0.079 1.22
N=L=1024
1.6GHz
2GHz
3GHz
1.6GHz and 2.0 GHz (6 TS)3 GHz (1 TS)
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
A Case Study for Space Weather
June 6, 2000 solar flares (X2.3) 15:00-16:35 UT (NOAA AR 9026)
Var[C(L)] = (1/N)i (Ci - C)2
1min before the flare
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)
Thank you for your attention.
LAC - CTE
http://epacis.org www.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)
V=9
g=9
=16
=20
Time Series Analysis (High sensitivity): Gradient Pattern Analysis
“Asymmetry Coefficient”: G= ( - g)/g and Limg G=2
Assireu et al, Physica D 168(1):397, 2002.
G=1.87 G=1.82
Escala global
Escala local
G= ( - g)/gG(ℓ)
• 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)
Gradient Spectra for Turbulent-like Short Time Series
G = 1/N [Gi (ℓ)-G(ℓ]