Abstract.

During the past decade, Georgia State University’s (GSU) Data Mining Lab (DMLab) has been conducting research on a wide range of topics centering on understanding, detection, and forecast of solar events, those of which can (directly or indirectly) have significant economic and collateral impacts on mankind, through electromagnetic radiation and energetic particles. The close collaboration of the Computer Scientists and Solar Physicists with the sole dedication to research on solar events using advanced statistical tools, machine learning (ML) and deep learning (DL), resulted in a couple of hundreds of in-depth studies in this domain. Many of these studies have been published in prestigious journals such as Nature’s Scientific Data and The Astrophysical Journal. We would like to prepare a tutorial on some of the methodologies we engineered, the challenges we faced, and the products we put together. We believe our solutions and products can stimulate new data-driven discoveries in heliophysics, as well as to serve and inspire communities of other domains.

[http://bigdataieee.org/BigData2020/Tutorials.html#tutorial6][Slides]

Abstract.

The goal of this dataset competition is to introduce the machine learning/data mining community to an integrated dataset that can be utilized for predicting and understanding solar flares. Solar flares and Coronal Mass Ejections (CMEs) are events occurring in the solar corona and heliosphere that can have a major negative impact on our technology dependent society. Electromagnetic radiation and ionized particles from solar flares and eruptions tend to be filtered out by Earth’s atmosphere, but they can still pose a hazard to astronauts and sensitive equipment in space, as well as disrupt various high frequency radio communications that military and civilian customers become increasingly reliant upon each year. A strong enough CME can also cause significant enough fluctuations in Earth’s magnetosphere to induce currents in large networks of conductive materials such as power grids. These induced currents can lead to surges that have the potential to melt transformers of long distance transmission lines causing large scale blackouts. A 2008 report by the National Research Council concluded that a solar superstorm, similar to one observed in 1857 called the Carrington event, could cripple the entire US power grid for months and lead to an economic damage of 1 to 2 trillion dollars.

[https://dmlab.cs.gsu.edu/bigdata/flare-comp-2020/]

Abstract.

The goal of this dataset competition is to introduce the machine learning/data mining community to an integrated dataset that can be utilized for predicting and understanding solar flares. Solar flares and Coronal Mass Ejections (CMEs) are events occurring in the solar corona and heliosphere that can have a major negative impact on our technology dependent society. Electromagnetic radiation and ionized particles from solar flares and eruptions tend to be filtered out by Earth’s atmosphere, but they can still pose a hazard to astronauts and sensitive equipment in space, as well as disrupt various high frequency radio communications that military and civilian customers become increasingly reliant upon each year. A strong enough CME can also cause significant enough fluctuations in Earth’s magnetosphere to induce currents in large networks of conductive materials such as power grids. These induced currents can lead to surges that have the potential to melt transformers of long distance transmission lines causing large scale blackouts. A 2008 report by the National Research Council concluded that a solar superstorm, similar to one observed in 1857 called the Carrington event, could cripple the entire US power grid for months and lead to an economic damage of 1 to 2 trillion dollars.

[https://dmlab.cs.gsu.edu/bigdata/flare-comp-2019/]