An automated classification approach to ranking photospheric proxies of magnetic energy build-up

We study the photospheric magnetic field of ~2000 active regions in solar cycle 23 to search for parameters indicative of energy build-up and subsequent release as a solar flare. We extract three sets of parameters: snapshots in space and time- total flux, magnetic gradients, and neutral lines; evolution in time- flux evolution; structures at multiple size scales- wavelet analysis. This combines pattern recognition and classification techniques via a relevance vector machine to determine whether a region will flare. We consider classification performance using all 38 extracted features and several feature subsets. Classification performance is quantified using both the true positive rate and the true negative rate. Additionally, we compute the true skill score which provides an equal weighting to true positive rate and true negative rate and the Heidke skill score to allow comparison to other flare forecasting work. We obtain a true skill score of ~0.5 for any predictive time window in the range 2-24hr, with a TPR of ~0.8 and a TNR of ~0.7. These values do not appear to depend on the time window, although the Heidke skill score (<0.5) does. Features relating to snapshots of the distribution of magnetic gradients show the best predictive ability over all predictive time windows. Other gradient-related features and the instantaneous power at various wavelet scales also feature in the top five ranked features in predictive power. While the photospheric magnetic field governs the coronal non-potentiality (and likelihood of flaring), photospheric magnetic field alone is not sufficient to determine this uniquely. Furthermore we are only measuring proxies of the magnetic energy build up. We still lack observational details on why energy is released at any particular point in time. We may have discovered the natural limit of the accuracy of flare predictions from these large scale studies

Evidence of a Plasmoid-Looptop Interaction and Magnetic Inflows During a Solar Flare/CME Eruptive Event

Observational evidence is presented for the merging of a downward-propagating plasmoid with a looptop kernel during an occulted limb event on 2007 January 25. RHESSI lightcurves in the 9-18 keV energy range, as well as that of the 245 MHz channel of the Learmonth Solar Observatory, show enhanced nonthermal emission in the corona at the time of the merging suggesting that additional particle acceleration took place. This was attributed to a secondary episode of reconnection in the current sheet that formed between the two merging sources. RHESSI images were used to establish a mean downward velocity of the plasmoid of 12 km/s. Complementary observations from the SECCHI suite of instruments onboard STEREO-Behind showed that this process occurred during the acceleration phase of the associated CME. From wavelet-enhanced EUVI, images evidence of inflowing magnetic field lines prior to the CME eruption is also presented. The derived inflow velocity was found to be 1.5 km/s. This combination of observations supports a recent numerical simulation of plasmoid formation, propagation and subsequent particle acceleration due to the tearing mode instability during current sheet formation.Comment: 8 pages, 9 figures, ApJ (Accepted

Automated Detection of Coronal Loops using a Wavelet Transform Modulus Maxima Method

We propose and test a wavelet transform modulus maxima method for the au- tomated detection and extraction of coronal loops in extreme ultraviolet images of the solar corona. This method decomposes an image into a number of size scales and tracks enhanced power along each ridge corresponding to a coronal loop at each scale. We compare the results across scales and suggest the optimum set of parameters to maximise completeness while minimising detection of noise. For a test coronal image, we compare the global statistics (e.g., number of loops at each length) to previous automated coronal-loop detection algorithms