cherenkov instability
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Author(s):  
Andreas Ipp ◽  
David I. Müller

Abstract We review our progress on 3+1D Glasma simulations to describe the earliest stages of heavy-ion collisions. In our simulations we include nuclei with finite longitudinal extent and describe the collision process as well as the evolution of the strongly interacting gluonic fields in the laboratory frame in 3+1 dimensions using the colored particle-in-cell method. This allows us to compute the 3+1 dimensional Glasma energy-momentum tensor, whose rapidity dependence can be compared to experimental pion multiplicity data from RHIC. An improved scheme cures the numerical Cherenkov instability and paves the way for simulations at higher energies used at LHC.


2020 ◽  
Vol 102 (1) ◽  
Author(s):  
Manuel Kirchen ◽  
Remi Lehe ◽  
Soeren Jalas ◽  
Olga Shapoval ◽  
Jean-Luc Vay ◽  
...  

2016 ◽  
Vol 94 (5) ◽  
Author(s):  
Remi Lehe ◽  
Manuel Kirchen ◽  
Brendan B. Godfrey ◽  
Andreas R. Maier ◽  
Jean-Luc Vay

2016 ◽  
Vol 09 ◽  
pp. 165-186 ◽  
Author(s):  
Jean-Luc Vay ◽  
Rémi Lehe

Computer simulations have had a profound impact on the design and understanding of past and present plasma acceleration experiments, and will be a key component for turning plasma accelerators from a promising technology into a mainstream scientific tool. In this article, we present an overview of the numerical techniques used with the most popular approaches to model plasma-based accelerators: electromagnetic particle-in-cell, quasistatic and ponderomotive guiding center. The material that is presented is intended to serve as an introduction to the basics of those approaches, and to advances (some of them very recent) that have pushed the state of the art, such as the optimal Lorentz-boosted frame, advanced laser envelope solvers and the elimination of numerical Cherenkov instability. The particle-in-cell method, which has broader interest and is more standardized, is presented in more depth. Additional topics that are cross-cutting, such as azimuthal Fourier decomposition or filtering, are also discussed, as well as potential challenges and remedies in the initialization of simulations and output of data. Examples of simulations using the techniques that are presented have been left out of this article for conciseness, and because simulation results are best understood when presented together, and contrasted with theoretical and/or experimental results, as in other articles of this volume.


2014 ◽  
Vol 267 ◽  
pp. 1-6 ◽  
Author(s):  
Brendan B. Godfrey ◽  
Jean-Luc Vay

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