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| Paper: |
Detecting Bright Points in Hinode XRT Lightcurves |
| Volume: |
456, The Fifth Hinode Science Meeting |
| Page: |
211 |
| Authors: |
Posson-Brown, J.; Kashyap, V.; Grigis, P. |
| Abstract: |
One of the greatest challenges in solar coronal physics is to obtain a
statistically complete sample of short duration events like coronal
bright points. Such samples are necessary to fully characterize the
properties of these events and understand the physical basis of such
phenomena. Datasets are best acquired automatically, without manual
intervention, in order to avoid introducing observer biases. We
evaluate several algorithms for detecting flare events in time series
data. One algorithm determines where derivatives of the
Gaussian-smoothed lightcurve cross certain thresholds. A second
algorithm segments the Loess-smoothed lightcurve between consecutive
minima, then joins adjacent segments if their extrema are not
statistically distinguishable. A third algorithm is a hybrid of the
first two. We generate simulated datasets with similar properties to
observed Hinode XRT quiet Sun lightcurves and test each algorithm on
these datasets. The performance of each algorithm on the simulated
lightcurves is scored according to the rates of false positive (Type
I) and false negative (Type II) errors. We use these results to
optimize the parameter values of each algorithm. We compare the
performances of the algorithms and evaluate the efficiency with which
they are able to detect small events. Such evaluations are relevant
to properly interpret the observed steepening of the slope of the
solar flare energy distribution at small energies. |
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