Projects per year
Abstract
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.
Original language | English |
---|---|
Article number | 34 |
Number of pages | 8 |
Journal | Frontiers in Astronomy and Space Sciences |
Volume | 7 |
DOIs | |
Publication status | Published - 26 Jun 2020 |
Keywords
- Convolutional neural network
- Solar flares
- Flare ribbons
- Machine learning
- Classification
- Helio19
Fingerprint
Dive into the research topics of 'Analyzing AIA flare observations using convolutional neural networks'. Together they form a unique fingerprint.Projects
- 1 Finished
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Solar and Magnetospheric: Solar and Magnetospheric Magnetohydrodynamics and Plasmas: Theory and Application
Hood, A. W. (PI), Archontis, V. (CoI), De Moortel, I. (CoI), Mackay, D. H. (CoI), Neukirch, T. (CoI), Parnell, C. E. (CoI) & Wright, A. N. (CoI)
Science & Technology Facilities Council
1/04/19 → 31/03/22
Project: Standard
Datasets
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Analysing AIA Flare Observations using Convolutional Neural Networks (Datasets)
Love, T. (Creator), Neukirch, T. (Contributor) & Parnell, C. E. (Contributor), University of St Andrews, 2020
DOI: 10.17630/fa62b9e5-4bd5-4c35-82db-4910d3df62f5
Dataset
File