A Data-Driven Phase-Space View of Sub-Alfvénic Magnetic-Cloud Coupling

Sub-Alfvénic solar wind intervals predominantly transpire into the core of magnetic clouds (MC) during interplanetary coronal mass ejection (ICME) events, facilitating an intense mode of solar wind-magnetosphere interaction wherein energy and information can propagate via magnetic field lines. These phenomena are associated with intense magnetic fields, low plasma beta, heightened Alfvénic activity, and exceptionally effective energy transfer to the magnetospheric domain. This study employs a physics-informed machine learning framework to identify and characterize the sub-Alfvénic magnetic cloud regime using data from many solar cycles. A feature space motivated by physical principles is established based on the plasma characteristics of upstream solar wind observed from the L1 point, along with metrics of wave activity obtained from time-frequency analysis. Employing unsupervised machine learning, the high-dimensional solar-wind feature space is mapped onto a low-dimensional latent space that elucidates the intrinsic organization of solar-wind plasma regimes. By integrating recognized MC occurrences and disparate individual case studies of sub-Alfvénic flow onto the established phase-space map, it has been deduced that severe coupling conditions are indicative of a cohesive global regime of solar wind behavior rather than isolated anomalies. This framework also illustrates transition paths among background solar wind, sheaths, and magnetic cloud cores, utilizing the evolution of coupling conditions during interplanetary coronal mass ejection passages.