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Abstract In this second paper in a series dedicated to characterizing shear layers via 2D hybrid (kinetic ions–fluid electrons) simulations, we study the dynamical role of nonthermal particles (cosmic rays, CRs), either spontaneously generated or preexisting. We initialize Kolmogorov-type sinusoidal velocity shear flows unstable to the Kelvin–Helmholtz instability, which evolve nonlinearly into turbulence. Particles with large gyroradii act as long-range messengers that promote momentum exchange between layers, hence introducing a form of CR viscosity. Even when not energetically dominant, increasing the CR energy density generally enhances momentum transfer, provided that their gyroradii are smaller than the shear length scale. We consider flows ranging from subsonic to supersonic and assess the rate of shear dissipation, the partition of the initial kinetic energy among heating, thermal ion acceleration, CR reacceleration, magnetic-field amplification, and the maximum energy attained by accelerated particles.
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This content will become publicly available on June 1, 2027
Deep learning analysis of ions accelerated at shocks
Abstract We study the application of deep learning techniques to the analysis and classification of ions accelerated at collisionless shocks in hybrid (kinetic ions-fluid electrons) simulations.Ions were classified as thermal, suprathermal, or non-thermal, depending on the energy they achieved and the acceleration regime they fell under. These classifications were used to train deep learning models to predict which particles are injected into the acceleration process with high accuracy (>90%), using only time series ofthe localmagnetic or electric field they experienced during their initial interaction with the shock.An autoencoder architecture was also tested, for which time series of various parameters were reconstructed from encoded representations.This study shows the potential of applying machine learning techniques to extract physical insights from kinetic plasma simulations and sets the groundwork for future applications, including the construction of sub-grid models in fluid approaches.
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- Award ID(s):
- 2510951
- PAR ID:
- 10700682
- Publisher / Repository:
- JCAP
- Date Published:
- Journal Name:
- Journal of Cosmology and Astroparticle Physics
- Volume:
- 2026
- Issue:
- 06
- ISSN:
- 1475-7516
- Page Range / eLocation ID:
- 071
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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