arXiv subjects

Alexander Sinn

Publications and source records attributed to Alexander Sinn.

8 recordsLinked to original sources

arXiv2026

Gridless Quasistatic Model for Efficient Simulation of Plasma-based Accelerators

The accurate modeling of plasma-based accelerators relies on costly numerical simulations due to the complexity of laser-plasma and beam-plasma interactions. Several strategies can highly reduce the computational cost compared to 3D first-principles particle-in-cell simulations, such as exploiting the near axial symmetry and quasistatic nature of plasma wakefields in many practical cases. Here, we propose a quasistatic algorithm that enables the modeling of axially symmetric plasma wakes without the need of a numerical grid. The gridless approach allows extremely fine features to be resolved without a dramatic increase in computational cost. This is critical, e.g., for the design of future plasma-based colliders with nanometer emittance beams. The proposed model has been implemented in the Wake-T code, where it is coupled to a laser envelope solver and a particle beam pusher to enable the efficient simulation of laser- and beam-driven plasma accelerators.

physics.plasm-ph↗

arXiv2024

LASY: LAser manipulations made eaSY

Using realistic laser profiles for simulations of laser-plasma interaction is critical to reproduce experimental measurements, but the interface between experiments and simulations can be challenging. Similarly, start-to-end simulations with different codes may require error-prone manipulations to convert between different representations of a laser pulse. In this work, we propose LASY, an open-source Python library to simplify these workflows. Developed through an international collaboration between experimental, theoretical and computational physicists, LASY can be used to initialize a laser profile from an experimental measurement, from a simulation, or from analytics, manipulate it, and write it into a file in compliance with the openPMD standard. This profile can then be used as an input of a simulation code.

physics.optics↗

arXiv2024

AMReX and pyAMReX: Looking Beyond ECP

AMReX is a software framework for the development of block-structured mesh applications with adaptive mesh refinement (AMR). AMReX was initially developed and supported by the AMReX Co-Design Center as part of the U.S. DOE Exascale Computing Project, and is continuing to grow post-ECP. In addition to adding new functionality and performance improvements to the core AMReX framework, we have also developed a Python binding, pyAMReX, that provides a bridge between AMReX-based application codes and the data science ecosystem. pyAMReX provides zero-copy application GPU data access for AI/ML, in situ analysis and application coupling, and enables rapid, massively parallel prototyping. In this paper we review the overall functionality of AMReX and pyAMReX, focusing on new developments, new functionality, and optimizations of key operations. We also summarize capabilities of ECP projects that used AMReX and provide an overview of new, non-ECP applications.

cs.DC↗

arXiv2024

Resonant emittance mixing of flat beams in plasma accelerators

Linear colliders rely on high-quality flat beams to achieve the desired event rate, while avoiding potentially deleterious beamstrahlung effects. Here, we show that flat beams in plasma accelerators can be subject to quality degradation due to emittance mixing. This effect occurs when the beam particles' betatron oscillations in a nonlinearly coupled wakefield become resonant in the horizontal and vertical planes. Emittance mixing can lead to a substantial decrease of the luminosity, the main quantity determining the event rate. In some cases, the use of laser drivers or flat particle beam drivers may decrease the fraction of resonant particles and, hence, mitigate emittance deterioration.

physics.acc-ph↗

arXiv2023

Emittance-preserving acceleration of high-quality positron beams using warm plasma filaments

Preserving the quality of positron beams in plasma-based accelerators, where wakefields are generated in electron filaments, is challenging. These wakefields are characterized by transversely non-linear focusing fields and non-uniform accelerating fields. However, a nonzero plasma temperature linearizes the transverse wakefield within the central region of the electron filament. In this study, we employ 3D particle-in-cell simulations with mesh refinement to demonstrate that beams with emittances on the order of tens of nanometers are contained within the linearized region of the transverse wakefield. This enables emittance preservation to one percent, while positron beams with the same charge and micrometer emittances, which sample the non-linear part of the transverse wakefield, experience a relative emittance growth of ten percent. Additionally, we observe a significant reduction in the growth rate of the slice energy spread for the tens of nanometers emittance beams in comparison to the micrometer emittance beams. The utilization of warm plasmas in conjunction with low-emittance beams opens up new avenues for enhancing the beam quality across various plasma-based positron acceleration approaches.

physics.acc-ph↗

arXiv2023

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.

physics.acc-ph↗

arXiv2022

Fast Reconstruction of Three-Quarter Sampling Measurements Using Recurrent Local Joint Sparse Deconvolution and Extrapolation

Recently, non-regular three-quarter sampling has shown to deliver an increased image quality of image sensors by using differently oriented L-shaped pixels compared to the same number of square pixels. A three-quarter sampling sensor can be understood as a conventional low-resolution sensor where one quadrant of each square pixel is opaque. Subsequent to the measurement, the data can be reconstructed on a regular grid with twice the resolution in both spatial dimensions using an appropriate reconstruction algorithm. For this reconstruction, local joint sparse deconvolution and extrapolation (L-JSDE) has shown to perform very well. As a disadvantage, L-JSDE requires long computation times of several dozen minutes per megapixel. In this paper, we propose a faster version of L-JSDE called recurrent L-JSDE (RL-JSDE) which is a reformulation of L-JSDE. For reasonable recurrent measurement patterns, RL-JSDE provides significant speedups on both CPU and GPU without sacrificing image quality. Compared to L-JSDE, 20-fold and 733-fold speedups are achieved on CPU and GPU, respectively.

eess.IV↗

arXiv2021

HiPACE++: a portable, 3D quasi-static Particle-in-Cell code

Modeling plasma accelerators is a computationally challenging task and the quasi-static particle-in-cell algorithm is a method of choice in a wide range of situations. In this work, we present the first performance-portable, quasi-static, three-dimensional particle-in-cell code HiPACE++. By decomposing all the computation of a 3D domain in successive 2D transverse operations and choosing appropriate memory management, HiPACE++ demonstrates orders-of-magnitude speedups on modern scientific GPUs over CPU-only implementations. The 2D transverse operations are performed on a single GPU, avoiding time-consuming communications. The longitudinal parallelization is done through temporal domain decomposition, enabling near-optimal strong scaling from 1 to 512 GPUs. HiPACE++ is a modular, open-source code enabling efficient modeling of plasma accelerators from laptops to state-of-the-art supercomputers.

physics.comp-ph↗