RAPID: Guided Laser Wakefield Acceleration at the ZEUS Laser Facility

Particle accelerators are powerful tools used in medicine, manufacturing, and fundamental science, but conventional accelerator facilities are large, expensive, and limited in number. This award advances a promising alternative: laser-driven particle accelerators that can achieve extremely high energies over much shorter distances by using plasma as the accelerating medium. The award supports the extension of this type of accelerator to unprecedented power and length scales on the highest power laser in the United States while leveraging machine learning in the design and optimization of the experiments. The upcoming experimental campaign at the NSF ZEUS laser user facility aims to uncover new physics and set new performance benchmarks for laser-driven accelerators. Beyond advancing fundamental knowledge, this work supports the long-term development of compact particle accelerators with potential applications in medical imaging and therapy, advanced radiation sources, and materials processing. The project will train graduate students and early-career scientists in cutting-edge experimental science at a major national facility, providing valuable experience in interdisciplinary research, data analysis, and scientific communication through publications and conference presentations. This RAPID award supports upcoming experiments at the NSF ZEUS multi-petawatt laser facility at the University of Michigan. These experiments aim to demonstrate >1 meter high-power laser guiding in plasma, investigate relativistic laser-plasma interaction at new, extreme scales, and acceleration of electrons to energies beyond 20 GeV. The award will support the development and characterization of specialized hardware for these experiments, including >1 meter supersonic gas jets with longitudinal density profiles, specialized optics for generating tailored plasma channels, and diagnostics for both the guiding and acceleration processes. Preparatory work will be carried out at the University of Maryland and will include novel use of artificial intelligence / machine learning techniques to optimize the channel design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.