Bayesian optimization of longitudinally aligned CNT targets for 800 nm laser wakefield acceleration in the bubble regime

Abstract

Laser wakefield acceleration (LWFA) in solid-density plasmas can reach accelerating gradients in the TeV/m range, well above those of gaseous media, provided the driver is matched to the plasma. In nanostructured solids, this matching can be achieved by controlling the spacing between the nanostructures to reduce the effective density, enabling infrared pulses to excite wakefields in the bubble regime. Following numerical demonstrations with carbon nanotube (CNT) bundles on concentric cylindrical shells, a simpler, experimentally more accessible longitudinally aligned CNT (LA-CNT) target is investigated here, with the bundles on a triangular lattice within a square cross section and an optional central aperture. Three-dimensional particle-in-cell simulations are coupled with Bayesian optimization to vary the effective density, target length, bundle radius and aperture radius for a fixed 100 mJ, few-cycle, 800 nm laser pulse, maximizing the laser-to-beam energy conversion efficiency for electrons above 20 MeV. With the aperture radius constrained to at least 0.5 $μ$m, the best target operates in the bubble regime and reaches an efficiency of 24.6%, delivering a 673 pC bunch with a mean energy of 36.6 MeV and a quasi-monoenergetic peak near 46 MeV, against 19% for the concentric-shell target under identical conditions. When the aperture is allowed to vanish, the optimization converges to denser and shorter targets, the efficiency peaks at 25.8%, and the best configurations deliver a higher charge at the cost of a lower peak energy and about 50% larger divergence; aperture-free targets among them retain a mean efficiency of 24.1%. The optimized targets are robust to random bundle displacements that preserve the effective density. By eliminating the need to pattern a micrometre-scale opening, aperture-free targets may facilitate proof-of-principle experiments on solid-state LWFA.