Machine Learning Characterization of Intermittency in Relativistic Pair Plasma Turbulence: Single and Double Sheet Structures
The physics of turbulence in magnetized plasmas remains an unresolved problem. The most poorly understood aspect is intermittency—spatiotemporal fluctuations superimposed on the self-similar turbulent motions. We employ a novel machine learning analysis technique to segment turbulent flow structures...
Saved in:
| Main Authors: | Trung Ha, Joonas Nättilä, Jordy Davelaar, Lorenzo Sironi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | The Astrophysical Journal Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/2041-8213/add47b |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Collisionless Tearing Instability in Relativistic Nonthermal Pair Plasma and Its Application to MHD Turbulence
by: Ivan Demidov, et al.
Published: (2025-01-01) -
MLody—Deep Learning–generated Polarized Synchrotron Coefficients
by: J. Davelaar
Published: (2024-01-01) -
Tearing-mediated Alfvénic Turbulence in a Relativistic Plasma
by: Stanislav Boldyrev, et al.
Published: (2025-01-01) -
The Contribution of Turbulent Active Galactic Nucleus Coronae to the Diffuse Neutrino Flux
by: Damiano F. G. Fiorillo, et al.
Published: (2025-01-01) -
Effective Resistivity in Relativistic Reconnection: A Prescription Based on Fully Kinetic Simulations
by: Abigail Moran, et al.
Published: (2025-01-01)