NDLI: Automatic Solar Flare Detection Using Neural Network Techniques

Content Provider	Springer Nature Link
Author	Fernandez Borda, Roberto A. Mininni, Pablo D. Mandrini, Cristina H. Gómez, Daniel O. Bauer, Otto H. Rovira, Marta G.
Copyright Year	2002
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 Hαimages. 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 (HαSolar 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 Hαinstrument and providing unique criteria for flare detection independent of the observer.
Starting Page	347
Ending Page	357
Page Count	11
File Format	PDF
ISSN	00380938
Journal	Solar Physics
Volume Number	206
Issue Number	2
e-ISSN	1573093X
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2002-01-01
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Astrophysics
Content Type	Text
Resource Type	Article
Subject	Astronomy and Astrophysics Space and Planetary Science

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