Automatic solar flare detection using neural network techniques

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 Hα images. Images are analyzed in real time, which allows for the design of automatic patrol processes...

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Autores principales: Fernández Borda, Roberto Armando, Mininni, Pablo Daniel, Mandrini, Cristina Hemilse, Gomez, Daniel Osvaldo, Rovira, Marta Graciela
Publicado: 2002
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00380938_v206_n2_p347_FernandezBorda
http://hdl.handle.net/20.500.12110/paper_00380938_v206_n2_p347_FernandezBorda
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_00380938_v206_n2_p347_FernandezBorda
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spelling paper:paper_00380938_v206_n2_p347_FernandezBorda2023-06-08T15:02:36Z Automatic solar flare detection using neural network techniques Fernández Borda, Roberto Armando Mininni, Pablo Daniel Mandrini, Cristina Hemilse Gomez, Daniel Osvaldo Rovira, Marta Graciela 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 Hα images. 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 (Hα Solar 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 Hα instrument and providing unique criteria for flare detection independent of the observer. Fil:Fernandez Borda, R.A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Mininni, P.D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Mandrini, C.H. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Gómez, D.O. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Rovira, M.G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2002 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00380938_v206_n2_p347_FernandezBorda http://hdl.handle.net/20.500.12110/paper_00380938_v206_n2_p347_FernandezBorda
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
description 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 Hα images. 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 (Hα Solar 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 Hα instrument and providing unique criteria for flare detection independent of the observer.
author Fernández Borda, Roberto Armando
Mininni, Pablo Daniel
Mandrini, Cristina Hemilse
Gomez, Daniel Osvaldo
Rovira, Marta Graciela
spellingShingle Fernández Borda, Roberto Armando
Mininni, Pablo Daniel
Mandrini, Cristina Hemilse
Gomez, Daniel Osvaldo
Rovira, Marta Graciela
Automatic solar flare detection using neural network techniques
author_facet Fernández Borda, Roberto Armando
Mininni, Pablo Daniel
Mandrini, Cristina Hemilse
Gomez, Daniel Osvaldo
Rovira, Marta Graciela
author_sort Fernández Borda, Roberto Armando
title Automatic solar flare detection using neural network techniques
title_short Automatic solar flare detection using neural network techniques
title_full Automatic solar flare detection using neural network techniques
title_fullStr Automatic solar flare detection using neural network techniques
title_full_unstemmed Automatic solar flare detection using neural network techniques
title_sort automatic solar flare detection using neural network techniques
publishDate 2002
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00380938_v206_n2_p347_FernandezBorda
http://hdl.handle.net/20.500.12110/paper_00380938_v206_n2_p347_FernandezBorda
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AT mandrinicristinahemilse automaticsolarflaredetectionusingneuralnetworktechniques
AT gomezdanielosvaldo automaticsolarflaredetectionusingneuralnetworktechniques
AT roviramartagraciela automaticsolarflaredetectionusingneuralnetworktechniques
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