nicholas shields sesi presentation – cua student brian dennis (mentor)
TRANSCRIPT
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FREQUENCY DISTRIBUTION OF GOES SOLAR FLARE PEAK FLUXES FROM 1994 TO 2005
Nicholas Shields
SESI Presentation – CUA Student
Brian Dennis (Mentor)
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OVERVIEW
Background on GOES satellites GOES Event list Size Distribution Fit power-law to the size distribution
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What is GOES?
Geostationary Operational Environmental SatellitesX-ray
Spectrometer 3 second data in
two wavelengths1-8 Angstrom0.5-4 Angstrom
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GOES Spectra Data:
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Event Detection
First we take the raw data and smooth it using either a boxcar smoothing or an average smoothing
The derivative of the data is taken Where the derivative crosses zero we
find either a peak or a valley
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Example of Event Detection:
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Data-drop/spike Filter: The SDAC data often has drops or spikes that we
do not want to declare peaks and valleys.
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Time Filter
The time intervals between a valley the next peak and the following valley are examined:
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Quantization Filter Quantization level =
flux value where the step size changes
Matches peak flux to quantization level
Difference between peak and valley flux values must be greater than quantization step * 3
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Effects of the Time & Quantization Filter:
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Background Subtraction: Method GOES satellite records even non-flaring
plasma Difficult to distinguish the flux levels of the
smaller solar flares
Linear background subtractionRuns from one valley to the nextIt takes the flux level at the first valley and
subtracts it from every point in the data array until reaching the next valley
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Example of Background Subtraction:
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Size Distribution
A size distribution was performed onun-subtracted databackground subtracted data.
Binned the data by the flare sizeShows the frequency of solar flares over
time based on their size
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Example of Size Distribution:
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Power-Law Fits
Used OSPEX (object spectral executive) Automatic fit using the closest
parameter settings Single power law fit
dN(p)/dp = A p−α
○ dN(p) is the number of events with a “size” between p and p + dp
○ A is a normalization parameter○ and α is the power-law index
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Power-Law Fits Example
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Past Results
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1994 to 2005 Results
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Work Still to be Done:
Change to creating a size distribution for a set number of flares rather than a set time interval
Use c-statistic to find the fit parameters rather than chi-squared
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Acknowledgements
Brian Dennis Andy Gopie Richard Schwartz Kim Tolbert Fred Bruhweiler