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Abstract Recent observations of the solar wind ions by the SPAN-I instruments on board the Parker Solar Probe (PSP) spacecraft at solar perihelia (Encounters) 4 and closer find ample evidence of complex anisotropic non-Maxwellian velocity distributions that consist of core, beam, and “hammerhead” (i.e., anisotropic beam) populations. The proton core populations are anisotropic, withT⊥/T∥ > 1, and the beams have super-Alfvénic speed relative to the core (we provide an example from Encounter 17). Theα-particle population shows similar features to the protons. These unstable velocity distribution functions (VDFs) are associated with enhanced, right-hand (RH) and left-hand (LH) polarized ion-scale kinetic wave activity, detected by the FIELDS instrument. Motivated by PSP observations, we employ nonlinear hybrid models to investigate the evolution of the anisotropic hot-beam VDFs and model the growth and the nonlinear stage of ion kinetic instabilities in several linearly unstable cases. The models are initialized with ion VDFs motivated by the observational parameters. We find rapidly growing (in terms of proton gyroperiods) combined ion-cyclotron and magnetosonic instabilities, which produce LH and RH ion-scale wave spectra, respectively. The modeled ion VDFs in the nonlinear stage of the evolution are qualitatively in agreement with PSP observations of the anisotropic core and “hammerhead” velocity distributions, quantifying the effect of the ion kinetic instabilities on wind plasma heating close to the Sun. We conclude that the wave–particle interactions play an important role in the energy transfer between the magnetic energy (waves) and random particle motion, leading to anisotropic solar wind plasma heating.
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This content will become publicly available on July 1, 2026
Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning
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 andα-particles initialized using plasma parameters derived from solar wind (SW) observations, we prepare a data set 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 (RF), with DL convolutional neural networks (CNNs) applied directly to VDFs as images in the gyrotropic velocity plane. The best-performing classifier, RF, has an accuracy of 0.96 ± 0.01, and a true skill score of 0.89 ± 0.03, 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 ± 0.18 among all considered if evaluated on the runs entirely not used during the model training. The addition of theE⊥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 SW and magnetospheric plasmas.
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- Award ID(s):
- 1936361
- PAR ID:
- 10634338
- Publisher / Repository:
- The Astrophysical Journal
- Date Published:
- Journal Name:
- The Astrophysical Journal Supplement Series
- Volume:
- 279
- Issue:
- 1
- ISSN:
- 0067-0049
- Page Range / eLocation ID:
- 28
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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