Speaker
Description
Laser-driven electron accelerators promise significant reductions in size and cost compared to their radiofrequency counterparts, unlocking opportunities for widespread use in hospitals, university labs, and beyond. Getting the most electron energy from such accelerators comes down to choosing just the right laser and plasma settings, which is a multi-parametric and highly nonlinear optimization problem. To address this challenge, we coupled computationally intensive particle-in-cell simulations with a Bayesian optimization algorithm. From the resulting data, we derived generalized scaling laws for electron energy, charge, and acceleration length as functions of laser energy. These scaling laws, together with the full set of input parameters, provide a practical framework for designing laser-driven electron acceleration experiments across a wide range of laser systems.
In this talk, we present the results obtained within the IT4Innovations Open Access Grant Competition projects OPEN-30-14 and OPEN-34-34, which have recently been published in [1, 2]. We discuss the use of the OPTIMAS library for Bayesian optimization (https://github.com/optimas-org/optimas) and the OSIRIS code for particle-in-cell simulations (https://github.com/osiris-code/osiris), highlighting our computational approaches, parallelization strategies, job scales, and the extent of resources employed at IT4Innovations.
This work was carried out in collaboration with Lawrence Livermore National Laboratory (USA), Princeton University (USA), University of Rochester (USA), and Kansai Institute for Photon Science (Japan). It was supported by the Defense Advanced Research Projects Agency (DARPA) under the Muons for Science and Security Program, by the project “e-INFRA CZ” (ID: 90254) from the Ministry of Education, Youth and Sports of the Czech Republic, as well as by the NSF–GACR collaborative grant (No. 2206059) and the Czech Science Foundation (Grant No. 22-42963L). A portion of the work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory (LLNL) under Contract No. DE-AC52-07NA27344, with additional support from the LLNL Institutional Computing Grand Challenge program.
[1] P. Valenta et al., Phys. Rev. Accel. Beams 28, 094601 (2025); https://doi.org/10.1103/knh7-hbr3.
[2] P. Valenta et al., Proc. SPIE 13534, 1353406 (2025); https://doi.org/10.1117/12.3058376.
