Abstract
Solar flares are one of the most extreme drivers of space weather in our solar system. The impulsive solar radio emission associated with a solar flare is known as a solar radio burst (SRB). They are generally studied in dynamic spectra and are classified into five major spectral classes, ranging from Type I to Type V, based on their form and frequency, and time duration. Due to their intricate characterisation, generating a training set for object-detection and classification models of such phenomena is a difficulty in machine learning. Current algorithms implement parametric modelling where the quantity, grouping, intensity, drift rate, heterogeneity, start–end frequency and start–end time of Type-III and Type-II radio bursts are all random. However, this model does not factor in the true shape or general features seen in real dynamic spectra observations of the Sun, which can be crucial when training classification or object-detection algorithms. In this research, we introduce a methodology named a Generative Adversarial Network (GAN) for generating realistic SRB simulations. By using real examples of Type-III and Type-II SRB data, we can train GANs to generate images almost comparable to real observed data. Furthermore, we evaluate the results of the generated model using human perception, then we compare and contrast the results using a metric known as the Fréchet Inception Distance.
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Code Availability
The implemented code for this research can be found here ...https://github.com/jeremiahscully/GANs.git.
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Acknowledgments
LOFAR is one of the largest astrophysics projects in Europe, consisting of 12 international stations spread across Germany, Poland, France, UK, Sweden and Ireland, with additional stations and a central hub in The Netherlands, operated by the Netherlands Institute for Radio Astronomy (ASTRON). I-LOFAR was the Irish addition to this network and was constructed by members from Trinity College Dublin (TCD), University College Dublin (UCD), Dublin City University (DCU), Dublin Institute of Technology (DIT) and National University of Ireland Galway (NUIG) with funding from Science Foundation Ireland (SFI), Department of Business, Enterprise and Innovation, Open Eir and Offaly County Council. J. Scully acknowledges support from SFI and the Technological University of the Shannon (TUS). We thank the anonymous reviewer for careful reading of our manuscript and many insightful comments and suggestions.
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The authors confirm their contribution to the paper as follows: J.S. prepared the study conception and design. J.S. carried out the data collection. J.S. developed the theory and performed the computations. J.S. performed the analysis and interpretation of the results. J.S. prepared the draft manuscript and figures. R.F. and M.D. carried out a critical revision of the article. All authors reviewed the results and approved the final version of the manuscript.
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Scully, J., Flynn, R., Carley, E. et al. Simulating Solar Radio Bursts Using Generative Adversarial Networks. Sol Phys 298, 6 (2023). https://doi.org/10.1007/s11207-022-02099-x
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DOI: https://doi.org/10.1007/s11207-022-02099-x