Abstract
The Solar Broadband Radio Spectrometer (SBRS) monitors the solar radio busts all day long and produces solar radio astronomical big data foranalysis every day, which usually have been accumulated in mass images for scientific study over decades. In the observed mass data, burst events are rare and always along with interference, so it seems impossible to identify whether the mass data contain bursts or not and figure out which type of burst it is by manual operation timely. Therefore, we take advantage of high performance computing and machine learning techniques to classify the huge volume astronomical imaging data automatically. The professional line of multiple NVIDIA GPUs has been exploited to deliver 78x faster parallel processing power for high performance computing of the astronomical big data, and neural networks have been utilized to learn the representations of the solar radio spectra. Experimental results have demonstrated that the employed network can effectively classify a solar radio image into the labeled categories. Moreover, the processing time is dramatically reduced by exploring GPU parallel computing environment.
L. Xu—This work was partially supported by a grant from the National Natural Science Foundation of China under Grant 61572461, 11433006 and CAS 100-Talents (Dr. Xu Long).
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Xu, L., Weng, Y., Chen, Z. (2016). Solar Radio Astronomical Big Data Classification. In: Xie, J., Chen, Z., Douglas, C., Zhang, W., Chen, Y. (eds) High Performance Computing and Applications. HPCA 2015. Lecture Notes in Computer Science(), vol 9576. Springer, Cham. https://doi.org/10.1007/978-3-319-32557-6_13
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DOI: https://doi.org/10.1007/978-3-319-32557-6_13
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