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Large flares and coronal mass ejections (CMEs) often result in dramatic consequences for the functioning of a wide range of ground-based (pipelines, power lines) and space-based infrastructures and services (satellites, GPS), as they may be seriously damaged. Therefore, the importance and needs of flare/CME forecasting is now rapidly increasing and approaches critical levels for the security of our technosphere.

First, I give a brief summary of how we could monitor the occurrence of solar flares and identify features that help predict this phenomenon. Being able to predict them in real-time is important because flares erupting on the Sun impact Earth over the course of minutes.

Nowadays, machine learning techniques provide a rapid, continuously updated overview of the state of the source region of flares, therefore, we also can get alert in real-time about an upcoming large flare/CME event. However, unfortunately, we cannot use machine learning techniques as a magic box in this research field, which we discuss and demonstrate through examples.

About the speaker

Dr Marianna Korsos, PDRA, Solar Research Group, Aberystwyth University

I have started my research career as an observing assistant at Debrecen Heliophysical Observatory, in Hungary. I moved to the UK when I had embarked, after a very short, 6-month, period of Research Assistant post, on my PhD studies at Sheffield in 2015.

After completing my PhD degree, I received my first 6-month long Postdoc position at the University of Sheffield. Next, I have started my new and more substantive Postdoc position at Aberystwyth University. Over the past few years, I have worked on developing a new and novel solar flare prediction method.

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Webinar: Predicting solar flares with machine learning
Date and time
Monday 14 March, 6:30pm - 8:00pm
This event is sold out