Speaker
Description
Jupiter and Saturn exemplify a unique magnetospheric paradigm defined by strong rotational driving and internal heavy-ion mass loading. In these systems, plasma accumulates into a dense inner torus before being propelled outwards via corotation-associated centrifugal forces. Magnetic flux is lost during the outward transport of the heavy plasma and is replaced via discrete interchange events (IEs), during which the relatively hot, tenuous, and “magnetically-buoyant” plasma of the outer inner-magnetosphere is transported inward. IEs manifest themselves through multiple possible in-situ signatures: events can encompass a depletion in low-energy (<~100 eV) plasma fluxes; an enhancement of higher-energy plasma fluxes; a sharp, few nT change in the magnetic field; and an enhancement of plasma wave activity across various wave types. IEs are foremost detectable by the first of these attributes, but any combination of these signatures may occur simultaneously over the 30s–few-minute IE period.
While interchange is integral to global mass circulation at the gas giants, plausible instability-onset mechanisms and the subsequent inflow morphologies remain poorly constrained due to the transient, single-point nature of existing spacecraft observations. Statistical evaluations of IE intervals are thus critical to characterizing event-time properties and inferring the spatial extent of event occurrence. However, the variability of IE-associated signatures has resulted in individual studies developing and employing different detection routines which consider and prioritize different instrument measurements, and, consequently, all observational interchange analyses have considered largely distinct sets of events. In this study, we apply a standard multi-instrument appraisal to a broad compilation of Jovian IE intervals that were identified across multiple existing Juno-era surveys to 1) assess if surveys are in fact identifying physically-alike events and 2) develop recommendations for more robust and standardized event detection. Our appraisal includes heavy-ion plasma properties which have not previously been considered in statistical analysis. We find that events can be separated into distinct classes, each of which is better organized by a unique multi-signature subset and dominates in a specific spatial region. Directional flow and ion composition properties are shown to display distinct behaviors that make them useful for detecting and categorizing event intervals, including some that had not previously been identified with other methods. We suggest possible applications of machine learning (ML) to bridge gaps in selection methodology, but we also discuss how certain ML implementations can potentially exacerbate detection biases and discrepancies. Our work provides insights that may be used to improve how we define and identify diverse signatures of plasma dynamics across heliophysical domains.