Identification of Ion-Kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning

TL;DR

This paper develops machine learning models to identify ion-kinetic instabilities in solar wind plasma simulations, achieving high accuracy and demonstrating potential for analyzing spacecraft data.

Contribution

It introduces ML and DL classifiers trained on hybrid-PIC simulation data to detect ion-scale kinetic instabilities with high accuracy.

Findings

Random Forest classifier accuracy: 96%.

CNN models achieve up to 88% accuracy on unseen data.

Adding spectral features improves instability detection.

Abstract

Analysis of ion-kinetic instabilities in solar wind plasmas is crucial for understanding energetics and dynamics throughout the heliosphere, as evident from spacecraft observations of complex ion velocity distribution functions (VDFs) and ubiquitous ion-scale kinetic waves. In this work, we explore machine learning (ML) and deep learning (DL) classification models to identify unstable cases of ion VDFs driving kinetic waves. Using 34 hybrid particle-in-cell simulations of kinetic protons and $\alpha$-particles initialized using plasma parameters derived from solar wind observations, we prepare a dataset of nearly 1600 VDFs representing stable/unstable cases and associated plasma and wave properties. We compare feature-based classifiers applied to VDF moments, such as Support Vector Machine and Random Forest, with DL convolutional neural networks (CNN) applied directly to VDFs as images in the gyrotropic velocity plane. The best-performing classifier, Random Forest, has an accuracy of $0.96\pm0.01$, and a true skill score of $0.89\pm0.03$, with the majority of missed predictions made near stability thresholds. We study how the variations of the temporal derivative thresholds of anisotropies and magnetic energies and sampling strategies for simulation runs affect classification. CNN-based models have the highest accuracy of $0.88\pm0.18$ among all considered if evaluated on the runs entirely not used during the model training. The addition of the $E_{\perp}$ power spectrum as an input for the ML models leads to the improvement of instability analysis for some cases. The results demonstrate the potential of ML and DL for the detection of ion-scale kinetic instabilities using spacecraft observations of solar wind and magnetospheric plasmas.