A Novel Feature Selection Method for Solar Flare Forecasting

Abstract

Large solar flares (SFs) can disrupt radio communication and harm instruments and astronauts. Hence, it's crucial to predict SFs. However, the mechanism that triggers SFs is not yet known. We only have several physical features believed to be related to the process. This makes choosing the most impactful features for SF production important. We investigate a feature selection method based on the weights learned by a linear classifier. We use the Spaceweather HMI Active Region Patch (SHARP) summary parameters based on the Solar Dynamics Observatory's Helioseismic and Magnetic Imager data records. The records are from May 2010 to December 2019. © 2024 IEEE.