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ABSTRACT The non-linear interaction between electromagnetic waves and plasmas attracts significant attention in astrophysics because it can affect the propagation of Fast Radio Bursts (FRBs) – luminous millisecond-duration pulses detected at radio frequency. The filamentation instability (FI) – a type of non-linear wave–plasma interaction – is considered to be dominant near FRB sources, and its non-linear development may also affect the inferred dispersion measure of FRBs. In this paper, we carry out fully kinetic particle-in-cell simulations of the FI in unmagnetized pair plasmas. Our simulations show that the FI generates transverse density filaments, and that the electromagnetic wave propagates in near vacuum between them, as in a waveguide. The density filaments keep merging until force balance between the wave ponderomotive force and the plasma pressure gradient is established. We estimate the merging time-scale and discuss the implications of filament merging for FRB observations.
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A machine learning based Bayesian optimization solution to non-linear responses in dusty plasmas
Abstract Nonlinear frequency response analysis is a widely used method for determining system dynamics in the presence of nonlinearities. In dusty plasmas, the plasma–grain interaction (e.g. grain charging fluctuations) can be characterized by a single-particle non-linear response analysis, while grain–grain non-linear interactions can be determined by a multi-particle non-linear response analysis. Here a machine learning-based method to determine the equation of motion in the non-linear response analysis for dust particles in plasmas is presented. Searching the parameter space in a Bayesian manner allows an efficient optimization of the parameters needed to match simulated non-linear response curves to experimentally measured non-linear response curves.
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- Award ID(s):
- 1740203
- PAR ID:
- 10326424
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 2
- Issue:
- 3
- ISSN:
- 2632-2153
- Page Range / eLocation ID:
- 035017
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/2632-2153/abe7b7
