Machine-Learning Characterization of Intermittency in Relativistic Pair Plasma Turbulence: Single and Double Sheet Structures
arXiv:2410.01878 · doi:10.3847/2041-8213/add47b
Abstract
The physics of turbulence in magnetized plasmas remains an unresolved problem. The most poorly understood aspect is intermittency -- spatio-temporal fluctuations superimposed on the self-similar turbulent motions. We employ a novel machine-learning analysis technique to segment turbulent flow structures into distinct clusters based on statistical similarities across multiple physical features. We apply this technique to kinetic simulations of decaying (freely evolving) and driven (forced) turbulence in a strongly magnetized pair-plasma environment, and find that the previously identified intermittent fluctuations consist of two distinct clusters: i) current sheets, thin slabs of electric current between merging flux ropes, and; ii) double sheets, pairs of oppositely polarized current slabs, possibly generated by two non-linearly interacting Alfvén-wave packets. The distinction is crucial for the construction of realistic turbulence sub-grid models.
17 pages, 10 figures. Accepted for publication in the Astrophysical Journal Letters
References in corpus (34)
- Scikit-learn: Machine Learning in Python
- Array Programming with NumPy
- Relativistic Reconnection: an Efficient Source of Non-Thermal Particles
- Spectrum of magnetohydrodynamic turbulence
- Compressible MHD Turbulence in Interstellar Plasmas
- Magnetic Reconnection and Hot Spot Formation in Black Hole Accretion Disks
- Particle Acceleration in Relativistic Plasma Turbulence
- Statistical Analysis of Current Sheets in Three-Dimensional Magnetohydrodynamic Turbulence
- The interplay of magnetically-dominated turbulence and magnetic reconnection in producing nonthermal particles
- Current Sheets and Collisionless Damping in Kinetic Plasma Turbulence
- Alfven Wave Collisions, The Fundamental Building Block of Plasma Turbulence I: Asymptotic Solution
- Refined critical balance in strong Alfvenic turbulence
- An Oscillating Langevin Antenna for Driving Plasma Turbulence Simulations
- Cosmic ray transport in large-amplitude turbulence with small-scale field reversals
- Spatially Localized Particle Energization by Landau Damping in Current Sheets Produced by Strong Alfven Wave Collisions
- The Dynamical Generation of Current Sheets in Astrophysical Plasma Turbulence
- Clustering of Intermittent Magnetic and Flow Structures near Parker Solar Probe's First Perihelion -- A Partial-Variance-of-Increments Analysis
- MHD instabilities in accretion disks and their implications in driving fast magnetic reconnection
- Alfven Wave Collisions, The Fundamental Building Block of Plasma Turbulence II: Numerical Solution
- Dynamic alignment and plasmoid formation in relativistic magnetohydrodynamic turbulence
- Generation of near-equipartition magnetic fields in turbulent collisionless plasmas
- Plasmoid Instability in the Multiphase Interstellar Medium
- Weak Alfvénic turbulence in relativistic plasmas II: Current sheets and dissipation
- Explosive reconnection of double tearing modes in relativistic plasmas: application to the Crab flares
- Nonlinear energy transfer and current sheet development in localized Alfven wavepacket collisions in the strong turbulence limit
- Weak Alfvénic turbulence in relativistic plasmas. Part 1. Dynamical equations and basic dynamics of interacting resonant triads
- Radiative plasma simulations of black hole accretion flow coronae in the hard and soft states
- Identifying magnetic reconnection in 2D Hybrid Vlasov Maxwell simulations with Convolutional Neural Networks
- Detecting Reconnection Events in Kinetic Vlasov Hybrid Simulations Using Clustering Techniques
- Intermittency and Dissipative Structures Arising from Relativistic Magnetized Turbulence
- Runko: Modern multiphysics toolbox for plasma simulations
- Electron-Scale Current Sheets and Energy Dissipation in 3D Kinetic-Scale Plasma Turbulence with Low Electron Beta
- Scale Statistics of Current Sheets in Relativistic Collisionless Plasma Turbulence
- aweSOM: a CPU/GPU-accelerated Self-organizing Map and Statistically Combined Ensemble Framework for Machine-learning Clustering Analysis