The accurate prediction of solar X-ray flux is a difficult problem due to noise and miscalibration of sensors, missing data, and the effects of the Earth’s position relative to the line of sight from the sensing satellite to the Sun. Most work on this problem has focused on predicting large sudden increases in flux known as solar flares that can have severe detrimental effects on human activities. Since solar flares are relatively rare and have heterogeneous characteristics, it has been difficult to train machine learning models for their prediction. In this work we approach the problem as regression rather than classification, addressing it as a time series prediction problem. We use X-ray data from the Geostationary Operational Environmental Satellite (GOES) gathered at one-minute intervals to predict the flux after time intervals ranging from one to five hours. We evaluate three deep neural network architectures — a convolutional neural network, Long Short-Term Memory, and a hierarchical dense residual network and compare with two conventional baselines. Our experiments show that all three architectures provide better results than the baseline algorithms, with the hierarchical residual network providing the best results.
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Predicting Solar X-ray Flux Using Deep Learning Techniques
Semantic Scholar · Physics · 2020
Abstract
The accurate prediction of solar X-ray flux is a difficult problem due to noise and miscalibration of sensors, missing data, and the effects of the Earth’s position relative to the line of sight from the sensing satellite to the Sun. Most work on this problem has focused on predicting large sudden increases in flux known as solar flares that can have severe detrimental effects on human activities. Since solar flares are relatively rare and have heterogeneous characteristics, it has been difficult to train machine learning models for their prediction. In this work we approach the problem as regression rather than classification, addressing it as a time series prediction problem. We use X-ray data from the Geostationary Operational Environmental Satellite (GOES) gathered at one-minute intervals to predict the flux after time intervals ranging from one to five hours. We evaluate three deep neural network architectures — a convolutional neural network, Long Short-Term Memory, and a hierarchical dense residual network and compare with two conventional baselines. Our experiments show that all three architectures provide better results than the baseline algorithms, with the hierarchical residual network providing the best results.