Predicting Solar Flares with Remote Sensing and Machine Learning

TL;DR

This paper reviews machine learning models for predicting solar flares using satellite data, emphasizing the importance of timely detection for safeguarding Earth's infrastructure and exploring edge computing for real-time analysis.

Contribution

It provides a survey of current ML models for solar flare prediction and discusses the potential of edge computing on satellites for rapid threat assessment.

Findings

Survey of open source solar flare prediction models

Highlighting edge computing for real-time analysis

Emphasizing the importance of continuous model training

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. Destruction of these critical systems would disable power grids and satellites, crippling communications and transportation. This would lead to food shortages and an inability to respond to emergencies. A solution to this impending problem is proposed herein using satellites in solar orbit that continuously monitor the Sun, use artificial intelligence and machine learning to calculate the probability of massive solar explosions from this sensed data, and then signal defense mechanisms that will mitigate the threat. With modern technology there may be only safeguards that can be implemented with enough warning, which is why the best algorithm must be identified and continuously trained with existing and new data to maximize true positive rates while minimizing false negatives. This paper conducts a survey of current machine learning models using open source solar flare prediction data. The rise of edge computing allows machine learning hardware to be placed on the same satellites as the sensor arrays, saving critical time by not having to transmit remote sensing data across the vast distances of space. A system of systems approach will allow enough warning for safety measures to be put into place mitigating the risk of disaster.