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Linear theory is a well developed framework for characterizing instabilitiesin weakly collisional plasmas, such as the solar wind. In the previousinstalment of this series, we analyzed ~1.5M proton and alpha particle VelocityDistribution Functions (VDFs) observed by Helios I and II to determine thestatistical properties of the standard instability parameters such as thegrowth rate, frequency, the direction of wave propagation, and the poweremitted or absorbed by each component, as well as to characterize theirbehavior with respect to the distance from the Sun and collisional processing.In this work, we use this comprehensive set of instability calculations totrain a Machine Learning algorithm consisting of three interlaced componentsthat: 1) predict if an interval is unstable from observed VDF parameters; 2)predict the instability properties for a given unstable VDF; and 3) classifythe type of the unstable mode. We use these methods to map the properties inmulti-dimensional phase space to find that the parallel-propagating,proton-core-induced Ion Cyclotron mode dominates the young solar wind, whilethe oblique Fast Magnetosonic mode regulates the proton beam drift in thecollisionally old plasma.

Ion-Driven Instabilities in the Inner Heliosphere II: Classification and Multi-Dimensional Mapping