Optimizing plasma-downramp profiles and beam transport for emittance preservation in multi-stage plasma accelerators

POSTER

Abstract

Plasma-based particle accelerators maintain accelerating fields that are several orders of magnitude higher than conventional accelerators. This allows for more compact accelerator footprints that can deliver particle beams of very high charge (> 100 pC) and large current (> kA) for various applications. For instance, plasma-wakefield accelerators are promising candidates for next-generation TeV-class electron-positron colliders for high-energy physics and secondary light sources. However, to reach the desired TeV energy regime, a staging approach of independent laser-driven plasma accelerators that each preserve low energy spread and beam emittance is required. Maintaining beam emittance over tens and hundreds of stages is a serious challenge but is crucial to achieving a high luminosity in future colliders. We present results for the optimization of plasma-stage downramp profiles and inter-stage beam transport in simulations of multi-stage plasma accelerators, carried out with codes from the Beam pLasma & Accelerator Simulation Toolkit (BLAST) and steered by optimas, a Python library for optimization at scale, powered by libEnsemble.

*This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics, General Accelerator R&D (GARD), under contract number DE-AC02-05CH11231. Supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program, and the Exascale Computing Project (17-SC-20-SC). This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.

Publication: AAC 2024 Proceedings Paper (submission not yet open)

Presenters

  • Marco Garten

    • Lawrence Berkeley National Laboratory

Authors

  • Marco Garten

    • Lawrence Berkeley National Laboratory
  • Remi Lehe

    • LBNL
  • Carlo Benedetti

    • Lawrence Berkeley National Laboratory
  • Ryan T Sandberg

    • Lawrence Berkeley National Laboratory
  • Olga Shapoval

    • Lawrence Berkeley National Laboratory (LBNL)
  • Axel Huebl

    • Lawrence Berkeley National Laboratory
  • Jean-Luc Vay

    • Lawrence Berkeley National Laboratory