Analyzing AIA Flare Observations Using Convolutional Neural Networks
In order to efficiently analyse the vast amount of data generated by solar space missions and ground-based instruments, modern machine learning techniques such as decision trees, support vector machines (SVMs) and neural networks can be very useful. In this paper we present initial results from usin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-06-01
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| Series: | Frontiers in Astronomy and Space Sciences |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fspas.2020.00034/full |
| _version_ | 1818117815796760576 |
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| author | Teri Love Thomas Neukirch Clare E. Parnell |
| author_facet | Teri Love Thomas Neukirch Clare E. Parnell |
| author_sort | Teri Love |
| collection | DOAJ |
| description | In order to efficiently analyse the vast amount of data generated by solar space missions and ground-based instruments, modern machine learning techniques such as decision trees, support vector machines (SVMs) and neural networks can be very useful. In this paper we present initial results from using a convolutional neural network (CNN) to analyse observations from the Atmospheric Imaging Assembly (AIA) in the 1,600Å wavelength. The data is pre-processed to locate flaring regions where flare ribbons are visible in the observations. The CNN is created and trained to automatically analyse the shape and position of the flare ribbons, by identifying whether each image belongs into one of four classes: two-ribbon flare, compact/circular ribbon flare, limb flare, or quiet Sun, with the final class acting as a control for any data included in the training or test sets where flaring regions are not present. The network created can classify flare ribbon observations into any of the four classes with a final accuracy of 94%. Initial results show that most of the images are correctly classified with the compact flare class being the only class where accuracy drops below 90% and some observations are wrongly classified as belonging to the limb class. |
| first_indexed | 2024-12-11T04:44:25Z |
| format | Article |
| id | doaj.art-4e17f373bf904eb38bd0a49561c32611 |
| institution | Directory Open Access Journal |
| issn | 2296-987X |
| language | English |
| last_indexed | 2024-12-11T04:44:25Z |
| publishDate | 2020-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Astronomy and Space Sciences |
| spelling | doaj.art-4e17f373bf904eb38bd0a49561c326112022-12-22T01:20:32ZengFrontiers Media S.A.Frontiers in Astronomy and Space Sciences2296-987X2020-06-01710.3389/fspas.2020.00034548393Analyzing AIA Flare Observations Using Convolutional Neural NetworksTeri LoveThomas NeukirchClare E. ParnellIn order to efficiently analyse the vast amount of data generated by solar space missions and ground-based instruments, modern machine learning techniques such as decision trees, support vector machines (SVMs) and neural networks can be very useful. In this paper we present initial results from using a convolutional neural network (CNN) to analyse observations from the Atmospheric Imaging Assembly (AIA) in the 1,600Å wavelength. The data is pre-processed to locate flaring regions where flare ribbons are visible in the observations. The CNN is created and trained to automatically analyse the shape and position of the flare ribbons, by identifying whether each image belongs into one of four classes: two-ribbon flare, compact/circular ribbon flare, limb flare, or quiet Sun, with the final class acting as a control for any data included in the training or test sets where flaring regions are not present. The network created can classify flare ribbon observations into any of the four classes with a final accuracy of 94%. Initial results show that most of the images are correctly classified with the compact flare class being the only class where accuracy drops below 90% and some observations are wrongly classified as belonging to the limb class.https://www.frontiersin.org/article/10.3389/fspas.2020.00034/fullsolar flaresribbonsmachine learningclassificationCNNs |
| spellingShingle | Teri Love Thomas Neukirch Clare E. Parnell Analyzing AIA Flare Observations Using Convolutional Neural Networks Frontiers in Astronomy and Space Sciences solar flares ribbons machine learning classification CNNs |
| title | Analyzing AIA Flare Observations Using Convolutional Neural Networks |
| title_full | Analyzing AIA Flare Observations Using Convolutional Neural Networks |
| title_fullStr | Analyzing AIA Flare Observations Using Convolutional Neural Networks |
| title_full_unstemmed | Analyzing AIA Flare Observations Using Convolutional Neural Networks |
| title_short | Analyzing AIA Flare Observations Using Convolutional Neural Networks |
| title_sort | analyzing aia flare observations using convolutional neural networks |
| topic | solar flares ribbons machine learning classification CNNs |
| url | https://www.frontiersin.org/article/10.3389/fspas.2020.00034/full |
| work_keys_str_mv | AT terilove analyzingaiaflareobservationsusingconvolutionalneuralnetworks AT thomasneukirch analyzingaiaflareobservationsusingconvolutionalneuralnetworks AT clareeparnell analyzingaiaflareobservationsusingconvolutionalneuralnetworks |