Events

Introduction

Solar, and by extension, stellar, activity is key to understanding the habitability of a planetary system. For example, it is unlikely that Earth would be habitable if the Sun were a T-Tauri rather than a G-type star. As a stable middle-aged star with a convective envelope, the Sun has settled into a roughly 22-year semi-periodic pattern of magnetic activity with only occasional major flare events. However, disruptions to this pattern, such as prolonged activity minima (i.e. Maunder minimum), have affected the climate, while unusually high levels of activity (i.e., the October-November 2003 period) can impact critical space infrastructure with worldwide consequences. Understanding long-term solar activity is beneficial to our society that is deeply entrenched in a technological age.

In order to extract societal and astrophysical relevant information from the myriad sources of observations, the development of many new processing algorithms is often required. Even with the deluge of petabytes of data retrieved from the fleet of observational tools available, these algorithms are often required to extract physical information from sparse data; an AI topic that is often overlooked in the age of Big Data science, yet, forms the main challenge in many, if not the majority, of astrophysics problems.

This workshop seeks to attract the interest of Big Data and Solar/Stellar Astrophysics researchers working to solve such problems in Solar & Stellar Astronomy Big Data, and solicits them to submit their research for publication. Innovative data mining and the application of big data-driven modeling techniques in these fields, including artificial intelligence and deep learning, are poised to address open research questions ranging from solar weather predictability to our place in the Universe.

Important Dates

• October 1, 2024: Due date for workshop paper submission

• November 4, 2024: Notification of paper acceptance

• November 20, 2024: Camera-ready version of accepted papers due

• December 15-18, 2024 (TBD) The exact workshop date will be determined as the conference approaches.

[https://agu.confex.com/agu/agu24/prelim.cgi/Session/228960]

Introduction

Solar, and by extension, stellar, activity is key to understanding the habitability of a planetary system. For example, it is unlikely that Earth would be habitable if the Sun were a T-Tauri rather than a G-type star. As a stable middle-aged star with a convective envelope, the Sun has settled into a roughly 22-year semi-periodic pattern of magnetic activity with only occasional major flare events. However, disruptions to this pattern, such as prolonged activity minima (i.e. Maunder minimum), have affected the climate, while unusually high levels of activity (i.e., the October-November 2003 period) can impact critical space infrastructure with worldwide consequences. Understanding long-term solar activity is beneficial to our society that is deeply entrenched in a technological age.

In order to extract societal and astrophysical relevant information from the myriad sources of observations, the development of many new processing algorithms is often required. Even with the deluge of petabytes of data retrieved from the fleet of observational tools available, these algorithms are often required to extract physical information from sparse data; an AI topic that is often overlooked in the age of Big Data science, yet, forms the main challenge in many, if not the majority, of astrophysics problems.

This workshop seeks to attract the interest of Big Data and Solar/Stellar Astrophysics researchers working to solve such problems in Solar & Stellar Astronomy Big Data, and solicits them to submit their research for publication. Innovative data mining and the application of big data-driven modeling techniques in these fields, including artificial intelligence and deep learning, are poised to address open research questions ranging from solar weather predictability to our place in the Universe.

Important Dates

• October 1, 2024: Due date for workshop paper submission

• November 4, 2024: Notification of paper acceptance

• November 20, 2024: Camera-ready version of accepted papers due

• December 15-18, 2024 (TBD) The exact workshop date will be determined as the conference approaches.

[https://sites.google.com/view/sepmeeting2024/home]

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/]