IAS Seminar "Deep and adversarial learning with high resolution solar images for space weather applications"

Speaker:

Dr. Frederic Johannes Effenberger, Helmholtz Zentrum Potsdam, GFZ German Research Centre for Geosciences

Abstract:

The Solar Dynamics Observatory (SDO) offers an unprecedented, very large dataset (TBs of raw data per day) of solar images in different optical and EUV wavelength bands, capturing solar atmospheric structures in high resolution and with excellent coverage and cadence since 2010. This dataset is thus well suited to study the application of advanced machine learning techniques that require large amounts of data for training, such as deep learning approaches. Here, we present our initial plans and results of deep learning as applied to solar images and discuss issues and pathways for future research. In particular, we address the scope for generative adversarial training and convolutional neural networks for data augmentation and space weather forecasting. Since ultimately, most of the space weather phenomena originate from solar activity, detailed solar images offer an excellent opportunity to improve on our predictive capabilities and utilize a large, high quality set of information encoded in image data.

Frederic Johannes Effenberger was invited by Dr. Jenia Jitsev (JSC).