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Sunspot Classification using Neural Networks

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posted on 2018-11-13, 10:18 authored by Shane MaloneyShane Maloney, Peter GallagherPeter Gallagher, Dean Power
Sunspots are the sources of the most extreme and potentially adverse solar events such as flares and coronal mass ejections (CMEs). As such many forecasting systems have been developed to predict these events, a number of which rely on sunspot group classifications. The classifications are manually produced so are subject to human errors and biases. Additionally, as the classifications are only produced on a daily basis this limits the time resolution of some forecasting methods. Further with the imaging cadence of SDO HMI, it would be impossible for a human to produce classifications for every observation. As such the development of an automated classification system would provide many benefits. Neural networks (NNs) have proven to be powerful tools for solving many complex problems such as classification, regression, and optimisation. In particular, the application of convolutional neural networks (CNNs) to image classification has greatly improved the performance of such systems. The first example of this, in the 1990s, was the identification of handwritten digits from 646 checks an 82% accuracy was achieved. Since then there have been numerous advances in both the network architectures and the underlying components. Recently an accuracy rate of 97.75% was achieved, identifying 1000 classes in 1,500,000 images for the ILSVRC2017 challenge. We applied a number of modern CNN architectures to the problem of classifying sunspots groups in SDO HMI observations. The input data consisted of sunspot regions extracted from SDO HMI magnetograms and the daily McIntosh or Mount Wilson sunspot classifications provided by the USAF/NOAA (2011-2018). We present the results of this work together with some issues encountered and avenues of further research.

Presented at SDO Science Meeting 2018 https://register-as.oma.be/sdo2018/

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