Stable discrete representation of relativistically drifting plasmas

Self-guiding of ultrashort, relativistically intense laser pulses through underdense plasmas in the blowout regime.

The self-guiding of relativistically intense but ultrashort laser pulses has been experimentally investigated as a function of laser power, plasma density, and plasma length in the blowout regime. The extent of self-guiding, observed by imaging the plasma exit, is shown to be limited by nonlinear pump depletion with observed self-guiding of over tens of Rayleigh lengths. Spectrally resolved ima...

Towards a Relativistically Correct Characterization of Counterstreaming Plasmas. I. Distribution Functions

The aim of this work is to provide a relativistically correct characterization for the stability of counterstreaming plasma structures ubiquitous in fusion plasma experiments and astrophysical sources of nonthermal radiation. Here, in the first part of this work, a new relativistically correct approach is formulated for the counterstreaming plasmas in thermal equilibrium, on the basis of the re...

A Representation for Characteristic Functionals of Stable Random Measures with Values in Sazonov Spaces

Dynamics of isotropic and drifting plasmas in the vicinity and within two-dimensional finite dust clusters

2D finite dust clusters are systems of small number of charged grains immersed in the plasma. The particle-in-cell (PIC) simulations are used to study the plasma structure, potential field and distributions of ion, electron and charge density within a dust cluster consisting of 19 grains in the absence and presence of a directed ion flow. In the isotropic plasma, an energetically stable configu...

Neural Discrete Representation Learning

Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector QuantisedVariational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt...