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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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