arXiv subjects

Elena S. Volnova

Publications and source records attributed to Elena S. Volnova.

3 recordsLinked to original sources

arXiv2026

Flow-Regulated Suprathermal Particle Acceleration in Weakly Collisional Astrophysical Plasmas

We investigate the formation of suprathermal particle populations in weakly collisional plasmas using a one-dimensional Fokker-Planck framework. A key element of this work is the introduction of a systematic velocity-space drift term that represents net energization relative to a background streaming flow. This term provides a minimal phenomenological description of competing relaxation and acceleration processes, enabling the incorporation of large-scale plasma dynamics into kinetic particle evolution. The model further includes spatial advection, velocity-space diffusion associated with wave-particle interactions, and collisional relaxation. To explore the role of time-dependent large-scale plasma dynamics, we consider three representative temporal profiles of the streaming velocity: accelerating, decelerating, and steady flows. We find that velocity-space diffusion primarily governs the formation of suprathermal tails, while the streaming-induced drift regulates their efficiency and spectral properties. In particular, the overall fraction of suprathermal particles depends only weakly on the detailed temporal evolution of the flow and is largely controlled by the time-averaged streaming velocity. These results suggest that large-scale streaming motions can be incorporated as an effective systematic energization mechanism in weakly collisional plasmas, providing a minimal and flexible framework applicable to a broad range of space and astrophysical environments.

physics.plasm-ph↗

arXiv2026

AI-based separation of turbulence from coherent background flows in decaying hydrodynamic turbulence

Separating turbulent fluctuations from coherent large-scale background flows is a longstanding challenge in the analysis of numerical simulations and astronomical observations. Traditional approaches commonly rely on decomposition-based techniques such as Fourier or wavelet filtering, which assume that a meaningful separation can be achieved through scale selection. In realistic flows, however, coherent motions and turbulence often overlap across a broad range of scales and interact nonlinearly, making a unique separation inherently ambiguous. In this work, we investigate the robustness of an AI-based turbulence-background separation approach using two-dimensional incompressible Navier-Stokes simulations of decaying hydrodynamic turbulence. The simulations are initialized with a coherent background flow and divergence-free turbulent perturbations with a Kolmogorov-like spectrum and evolve without external forcing, providing a controlled physical testbed. A neural network trained exclusively on static synthetic images is applied to simulation snapshots at different evolutionary stages. The model recovers turbulent fluctuations during early and intermediate stages when partial scale separation is present. At later stages, where nonlinear interactions increasingly mix coherent and turbulent structures, the separation becomes less distinct; nevertheless, the recovered fields remain visually and spectrally consistent with the expected turbulent behavior. Quantitative comparisons with a Fourier filtering baseline show that the AI-based approach achieves comparable reconstruction accuracy while not requiring an explicit spectral cutoff scale. These results suggest that AI models trained on static data can provide a flexible diagnostic tool for turbulence-background separation in time-evolving flows, with potential applications to astrophysical datasets.

physics.flu-dyn↗

arXiv2025

Electron Acceleration via Lower-Hybrid Drift Instability in Astrophysical Plasmas: Dependence on Plasma Beta and Suprathermal Electron Distributions

Density inhomogeneities are ubiquitous in space and astrophysical plasmas, particularly at magnetic reconnection sites, shock fronts, and within compressible turbulence. The gradients associated with these inhomogeneous plasma regions serve as free energy sources that can drive plasma instabilities, including the lower-hybrid drift instability (LHDI). Notably, lower-hybrid waves are frequently observed in magnetized space plasma environments, such as Earth's magnetotail and magnetopause. Previous studies have primarily focused on modeling particle acceleration via LHDI in these regions using a quasilinear approach. This study expands the investigation of LHDI to a broader range of environments, spanning weakly to strongly magnetized media, including interplanetary, interstellar, intergalactic, and intracluster plasmas. To explore the applicability of LHDI in various astrophysical settings, we employ two key parameters: (1) plasma magnetization, characterized by the plasma beta parameter, and (2) the spectral slope of suprathermal electrons following a power-law distribution. Using a quasilinear model, we determine the critical values of plasma beta and spectral slope that enable efficient electron acceleration via LHDI by comparing the rate of growth of instability and the damping rate of the resulting fluctuations. We further analyze the time evolution of the electron distribution function to confirm these critical conditions. Our results indicate that electron acceleration is generally most efficient in low-beta plasmas ($β< 1$). However, the presence of suprathermal electrons significantly enhances electron acceleration via LHDI, even in high-beta plasmas ($β> 1$). Finally, we discuss the astrophysical implications of our findings, highlighting the role of LHDI in electron acceleration across diverse plasma environments.

astro-ph.HE↗