A spectral, quasi-cylindrical and dispersion-free Particle-In-Cell algorithm

We propose a spectral Particle-In-Cell (PIC) algorithm that is based on the combination of a Hankel transform and a Fourier transform. For physical problems that have close-to-cylindrical symmetry, this algorithm can be much faster than full 3D PIC algorithms. In addition, unlike standard finite-difference PIC codes, the proposed algorithm is free of numerical dispersion. This algorithm is benchmarked in several situations that are of interest for laser-plasma interactions. These benchmarks show that it avoids a number of numerical artifacts, that would otherwise affect the physics in a standard PIC algorithm - including the zero-order numerical Cherenkov effect.Comment: 23 pages, 8 figure

Laser-plasma interactions with a Fourier-Bessel Particle-in-Cell method

A new spectral particle-in-cell (PIC) method for plasma modeling is presented and discussed. In the proposed scheme, the Fourier-Bessel transform is used to translate the Maxwell equations to the quasi-cylindrical spectral domain. In this domain, the equations are solved analytically in time, and the spatial derivatives are approximated with high accuracy. In contrast to the finite-difference time domain (FDTD) methods that are commonly used in PIC, the developed method does not produce numerical dispersion, and does not involve grid staggering for the electric and magnetic fields. These features are especially valuable in modeling the wakefield acceleration of particles in plasmas. The proposed algorithm is implemented in the code PLARES-PIC, and the test simulations of laser plasma interactions are compared to the ones done with the quasi-cylindrical FDTD PIC code CALDER-CIRC.Comment: submitted to Phys. Plasma

Demonstration of relativistic electron beam focusing by a laser-plasma lens

Laser-plasma technology promises a drastic reduction of the size of high energy electron accelerators. It could make free electron lasers available to a broad scientific community, and push further the limits of electron accelerators for high energy physics. Furthermore the unique femtosecond nature of the source makes it a promising tool for the study of ultra-fast phenomena. However, applications are hindered by the lack of suitable lens to transport this kind of high-current electron beams, mainly due to their divergence. Here we show that this issue can be solved by using a laser-plasma lens, in which the field gradients are five order of magnitude larger than in conventional optics. We demonstrate a reduction of the divergence by nearly a factor of three, which should allow for an efficient coupling of the beam with a conventional beam transport line

Particle-in-Cell Simulations of Relativistic Magnetic Reconnection with Advanced Maxwell Solver Algorithms

Relativistic magnetic reconnection is a non-ideal plasma process that is a source of non-thermal particle acceleration in many high-energy astrophysical systems. Particle-in-cell (PIC) methods are commonly used for simulating reconnection from first principles. While much progress has been made in understanding the physics of reconnection, especially in 2D, the adoption of advanced algorithms and numerical techniques for efficiently modeling such systems has been limited. With the GPU-accelerated PIC code WarpX, we explore the accuracy and potential performance benefits of two advanced Maxwell solver algorithms: a non-standard finite difference scheme (CKC) and an ultrahigh-order pseudo-spectral method (PSATD). We find that for the relativistic reconnection problem, CKC and PSATD qualitatively and quantitatively match the standard Yee-grid finite-difference method. CKC and PSATD both admit a time step that is 40% longer than Yee, resulting in a ~40% faster time to solution for CKC, but no performance benefit for PSATD when using a current deposition scheme that satisfies Gauss's law. Relaxing this constraint maintains accuracy and yields a 30% speedup. Unlike Yee and CKC, PSATD is numerically stable at any time step, allowing for a larger time step than with the finite-difference methods. We found that increasing the time step 2.4-3 times over the standard Yee step still yields accurate results, but only translates to modest performance improvements over CKC due to the current deposition scheme used with PSATD. Further optimization of this scheme will likely improve the effective performance of PSATD.Comment: 19 pages, 10 figures. Submitted to Ap

Exascale and ML Models for Accelerator Simulations

Computational modeling is essential to the exploration and design of advanced particle accelerators. The modeling of laser-plasma acceleration and interaction can achieve predictive quality for experiments if adequate resolution, full geometry and physical effects are included. Here, we report on the significant evolution in fully relativistic full-3D modeling of conventional and advanced accelerators in the WarpX and ImpactX codes with the introduction of Exascale supercomputing and AI/ML models. We will cover the first PIC simulations on an Exascale machine, the need for and evolution of open standards, and based on our fully open community codes, the connection of time and space scales from plasma to conventional beamlines with data-driven machine-learning models

From Compact Plasma Particle Sources to Advanced Accelerators with Modeling at Exascale

Developing complex, reliable advanced accelerators requires a coordinated, extensible, and comprehensive approach in modeling, from source to the end of beam lifetime. We present highlights in Exascale Computing to scale accelerator modeling software to the requirements set for contemporary science drivers. In particular, we present the first laser-plasma modeling on an exaflop supercomputer using the US DOE Exascale Computing Project WarpX. Leveraging developments for Exascale, the new DOE SCIDAC-5 Consortium for Advanced Modeling of Particle Accelerators (CAMPA) will advance numerical algorithms and accelerate community modeling codes in a cohesive manner: from beam source, over energy boost, transport, injection, storage, to application or interaction. Such start-to-end modeling will enable the exploration of hybrid accelerators, with conventional and advanced elements, as the next step for advanced accelerator modeling. Following open community standards, we seed an open ecosystem of codes that can be readily combined with each other and machine learning frameworks. These will cover ultrafast to ultraprecise modeling for future hybrid accelerator design, even enabling virtual test stands and twins of accelerators that can be used in operations.Comment: 4 pages, 3 figures, submitted to the 20th Advanced Accelerator Concepts Workshop (AAC22