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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 ≥ 1 day) observed at 1 au 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.

Magnetic flux ropes manifest as twisted bundles of magnetic field lines. They carry significant amounts of solar mass in the heliosphere. This paper underlines the need to advance our understanding of the fundamental physics of heliospheric flux ropes and provides the motivation to significantly improve the status quo of flux rope research through novel and requisite approaches. It briefly discusses the current understanding of flux rope formation and evolution, and summarizes the strategies that have been undertaken to understand the dynamics of heliospheric structures. The challenges and recommendations put forward to address them are expected to broaden the in-depth knowledge of our nearest star, its dynamics, and its role in its region of influence, the heliosphere.

Abstract The activity of the Sun alternates between a solar minimum and a solar maximum, the former corresponding to a period of “quieter” status of the heliosphere. During solar minimum, it is in principle more straightforward to follow eruptive events and solar wind structures from their birth at the Sun throughout their interplanetary journey. In this paper, we report analysis of the origin, evolution, and heliospheric impact of a series of solar transient events that took place during the second half of August 2018, that is, in the midst of the late declining phase of Solar Cycle 24. In particular, we focus on two successive coronal mass ejections (CMEs) and a following high‐speed stream (HSS) on their way toward Earth and Mars. We find that the first CME impacted both planets, whilst the second caused a strong magnetic storm at Earth and went on to miss Mars, which nevertheless experienced space weather effects from the stream interacting region preceding the HSS. Analysis of remote‐sensing and in‐situ data supported by heliospheric modeling suggests that CME–HSS interaction resulted in the second CME rotating and deflecting in interplanetary space, highlighting that accurately reproducing the ambient solar wind is crucial even during “simpler” solar minimum periods. Lastly, we discuss the upstream solar wind conditions and transient structures responsible for driving space weather effects at Earth and Mars.

Geomagnetic disturbance forecasting is based on the identification of solar wind structures and accurate determination of their magnetic field orientation. For nowcasting activities, this is currently a tedious and manual process. Focusing on the main driver of geomagnetic disturbances, the twisted internal magnetic field of interplanetary coronal mass ejections (ICMEs), we explore a convolutional neural network’s (CNN) ability to predict the embedded magnetic flux rope’s orientation once it has been identified from in situ solar wind observations. Our work uses CNNs trained with magnetic field vectors from analytical flux rope data. The simulated flux ropes span many possible spacecraft trajectories and flux rope orientations. We train CNNs first with full duration flux ropes and then again with partial duration flux ropes. The former provides us with a baseline of how well CNNs can predict flux rope orientation while the latter provides insights into real-time forecasting by exploring how accuracy is affected by percentage of flux rope observed. The process of casting the physics problem as a machine learning problem is discussed as well as the impacts of different factors on prediction accuracy such as flux rope fluctuations and different neural network topologies. Finally, results from evaluating the trained network against observed ICMEs from Wind during 1995–2015 are presented.