Is this project an undergraduate, graduate, or faculty project?
Graduate
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individual
What campus are you from?
Worldwide
Authors' Class Standing
Erik Larsen, Graduate Student
Lead Presenter's Name
E. Erik Larsen
Faculty Mentor Name
Dr. Jonathan W. Campbell
Abstract
High energy solar flares and coronal mass ejections have the potential to destroy Earth’s ground and satellite infrastructures, causing trillions of dollars in damage and mass human suffering. This would lead to food shortages and crippled emergency response capabilities. A solution to this impending problem is proposed herein using satellites in solar orbit with built-in machine learning capability that continuously monitor the Sun. They will use machine learning to calculate the probability of massive solar explosions from the remote sensing data, then signal defence mechanisms that can mitigate the threat. This paper reports the results from a survey of machine learning models using open-source solar flare prediction data. The rise of edge computing supports machine learning hardware placed on the same satellites as the sensor arrays, saving critical transmit time across the vast distances of space. A system of systems approach will allow enough warning for safety measures to be enacted, thus mitigating the risk of disaster.
Did this research project receive funding support from the Office of Undergraduate Research.
No
Predicting Solar Flares with Machine Learning
High energy solar flares and coronal mass ejections have the potential to destroy Earth’s ground and satellite infrastructures, causing trillions of dollars in damage and mass human suffering. This would lead to food shortages and crippled emergency response capabilities. A solution to this impending problem is proposed herein using satellites in solar orbit with built-in machine learning capability that continuously monitor the Sun. They will use machine learning to calculate the probability of massive solar explosions from the remote sensing data, then signal defence mechanisms that can mitigate the threat. This paper reports the results from a survey of machine learning models using open-source solar flare prediction data. The rise of edge computing supports machine learning hardware placed on the same satellites as the sensor arrays, saving critical transmit time across the vast distances of space. A system of systems approach will allow enough warning for safety measures to be enacted, thus mitigating the risk of disaster.