Automated recognition of solar flares in real-time data

Summary form only given. The focus of the automatic solar flare detection is on the development of efficient feature-based classifiers. The three principal techniques used in this work are multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) classifiers. We have experimented and compared these three methods for solar flare detection on the solar Hα (hydrogen-alpha) images obtained from the Big Bear Solar Observatory in California. The preprocessing step is to obtain the nine principal features of the solar flares for the classifiers. Experimental results show that by using SVM, we can obtain the best classification rate of the solar flares. Measurement of the evolution properties of solar flares through their complete cyclic development is also crucial in the studies of solar physics. From the analysis of solar images, we apply image segmentation techniques to compute the properties of solar flares. We also present our solution for automatically tracking the apparent separation motion of two-ribbon flares and measuring their moving direction and speed. We believe our work leads to real-time solar flare detection and characterization.