Automatic Detection of Large-scale Flux Ropes and Their Geoeffectiveness with a Machine-learning Approach

• Published in: The Astrophysical journal Vol. 972; no. 1; pp. 94 - 107
• Main Authors: Pal, Sanchita, G. dos Santos, Luiz F., Weiss, Andreas J., Narock, Thomas, Narock, Ayris, Nieves-Chinchilla, Teresa, Jian, Lan K., Good, Simon W.
• Format: Journal Article
• Language: English
• Published: Goddard Space Flight Center The American Astronomical Society 01.09.2024; American Astronomical Society; IOP Publishing
• Subjects: Astrophysics; Coronal mass ejection; Interplanetary magnetic fields; Machine learning; Magnetic fields; Magnetic properties; Nowcasting; Solar coronal mass ejections; Solar magnetic field; Solar wind; Solar wind properties; Space missions; Space weather; Visual fields; Visual observation; Weather forecasting; Wind data
• ISSN: 0004-637X; 1538-4357
• DOI: 10.3847/1538-4357/ad54c3

Detecting large-scale flux ropes (FRs) embedded in interplanetary coronal mass ejections (ICMEs) and assessing their geoeffectiveness are essential, since they can drive severe space weather. At 1 au, these FRs have an average duration of 1 day. Their most common magnetic features are large, smoothly rotating magnetic fields. Their manual detection has become a relatively common practice over decades, although visual detection can be time-consuming and subject to observer bias. Our study proposes a pipeline that utilizes two supervised binary classification machine-learning models trained with solar wind magnetic properties to automatically detect large-scale FRs and additionally determine their geoeffectiveness. The first model is used to generate a list of autodetected FRs. Using the properties of the southward magnetic field, the second model determines the geoeffectiveness of FRs. Our method identifies 88.6% and 80% of large-scale ICMEs (duration 1day) observed at 1au by the Wind and the Solar TErrestrial RElations Observatory missions, respectively. While testing with continuous solar wind data obtained from Wind, our pipeline detected 56 of the 64 large-scale ICMEs during the 2008–2014 period (recall = 0.875), but also many false positives (precision = 0.56), as we do not take into account any additional solar wind properties other than the magnetic properties. We find an accuracy of 0.88 when estimating the geoeffectiveness of the autodetected FRs using our method. Thus, in space-weather nowcasting and forecasting at L1 or any planetary missions, our pipeline can be utilized to offer a first-order detection of large-scale FRs and their geoeffectiveness.