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Interplanetary magnetic flux ropes (MFRs) are commonly observed structures inthe solar wind, categorized as magnetic clouds (MCs) and small-scale MFRs(SMFRs) depending on whether they are associated with coronal mass ejections.We apply machine learning to systematically compare SMFRs, MCs, and ambientsolar wind plasma properties. We construct a dataset of 3-minute averagedsequential data points of the solar wind's instantaneous bulk fluid plasmaproperties using about twenty years of measurements from \emph{Wind}. We labelsamples by the presence and type of MFRs containing them using a catalog basedon Grad-Shafranov (GS) automated detection for SMFRs and NASA's catalog for MCs(with samples in neither labeled non-MFRs). We apply the random forest machinelearning algorithm to find which categories can be more easily distinguishedand by what features. MCs were distinguished from non-MFRs with an AUC of 94%and SMFRs with an AUC of 89% and had distinctive plasma properties. Incontrast, while SMFRs were distinguished from non-MFRs with an AUC of 86%, thisappears to rely solely on the $\langle B \rangle$ > 5 nT threshold applied bythe GS catalog. The results indicate that SMFRs have virtually the same plasmaproperties as the ambient solar wind, unlike the distinct plasma regimes ofMCs. We interpret our findings as additional evidence that most SMFRs at 1 auare generated within the solar wind, and furthermore, suggesting that theyshould be considered a salient feature of the solar wind's magnetic structurerather than transient events.

A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 AU