Automatic Solar Flare Detection Using Neural Network Techniques

Abstract

We present a new method for automatic detection of flare events from images in the optical range. The method uses neural networks for pattern recognition and is conceived to be applied to full-disk Halphaimages. Images are analyzed in real time, which allows for the design of automatic patrol processes able to detect and record flare events with the best time resolution available without human assistance. We use a neural network consisting of two layers, a hidden layer of nonlinear neurodes and an output layer of one linear neurode. The network was trained using a back-propagation algorithm and a set of full-disk solar images obtained by HASTA (HalphaSolar Telescope for Argentina), which is located at the Estación de Altura Ulrico Cesco of OAFA (Observatorio Astronómico Félix Aguilar), El Leoncito, San Juan, Argentina. This method is appropriate for the detection of solar flares in the complete optical classification, being portable to any Halphainstrument and providing unique criteria for flare detection independent of the observer.