Stability (learning theory)

Physics

Electron

Collider

Energy (signal processing)

Plasma acceleration

Laser

Optics

Laser beam quality

Machine learning

Phase space

Beam (structure)

Atomic physics

Artificial intelligence

Particle accelerator

Nuclear physics

Ionization

Bayesian optimization

Computer science

Quantum mechanics

Plasma

Laser beams

Computational physics

Cathode ray

Physical Review Letters

Generating high-quality laser-plasma accelerated electron beams requires carefully balancing a plethora of physical effects and is therefore challenging---both conceptually and in experiments. Here, we use Bayesian optimization of key laser and plasma parameters to flatten the longitudinal phase space of an ionization-injected electron bunch via optimal beam loading. We first study the concept with particle-in-cell simulations and then demonstrate it in experiments. Starting from an arbitrary set point, the plasma accelerator autonomously tunes the beam energy spread to the subpercent level at 254 MeV and $4.7\text{ }\text{ }\mathrm{pC}/\mathrm{MeV}$ spectral density. Finally, we study a robust regime, which improves the stability of the laser-plasma accelerator and delivers sub-five-percent rms energy spread beams for 90% of all shots.

Bayesian Optimization of a Laser-Plasma Accelerator