Identifying Coronal Mass Ejection Active Region Sources: An Automated Approach

Abstract

Identifying the source regions of coronal mass ejections (CMEs) is crucial for understanding their origins and improving space weather forecasting. We present an automated algorithm for matching CMEs detected by the Large Angle Spectrometric Coronagraph with their source active regions, specifically Space Weather HMI Active Region Patches (SHARPs), between 2010 May and 2019 January. Our method uses posteruptive signatures, including flares and coronal dimmings, to associate CMEs with potential source regions. Out of 4190 CMEs, we successfully match 1132, achieving a recall rate of ~57% for frontside events. We find that the algorithm performs better for complex SHARP regions containing multiple NOAA regions and for faster CMEs, consistent with expectations that more energetic events produce stronger eruption signatures. We find that CME–flare association rates increase with flare intensity, aligning with previous studies. While our approach has limitations, such as focusing exclusively on SHARP regions and relying on a limited set of posteruptive signatures, it significantly reduces the time required for CME source identification while providing transparent, reproducible results. We encourage the solar physics community to build upon this work, developing improved automated tools for CME source identification. The resulting catalog of CME–source region associations is made publicly available, offering a valuable resource for statistical studies and machine learning applications in solar physics and space weather forecasting.