The generation of quasimonoenergetic electron beams, with energies up to 200 MeV, by a laser-plasma accelerator driven in a hydrogen-filled capillary discharge waveguide is investigated. Injection and acceleration of electrons is found to depend sensitively on the delay between the onset of the discharge current and the arrival of the laser pulse. A comparison of spectroscopic and interferometric measurements suggests that injection is assisted by laser ionization of atoms or ions within the channel.

The acceleration of electrons to '0:8 GeV has been observed in a self-injecting laser wakefield accelerator driven at a plasma density of 5:5 Â 10 18 cm À3 by a 10 J, 55 fs, 800 nm laser pulse in the blowout regime. The laser pulse is found to be self-guided for 1 cm (>10z R), by measurement of a single filament containing >30% of the initial laser energy at this distance. Three-dimensional particle in cell simulations show that the intensity within the guided filament is amplified beyond its initial focused value to a normalized vector potential of a 0 > 6, thus driving a highly nonlinear plasma wave.

Density down-ramp (DDR) injection is a promising concept in beam-driven plasma wakefield accelerators for the generation of high-quality witness beams. We review and complement the theoretical principles of the method and employ particle-in-cell (PIC) simulations in order to determine constrains on the geometry of the density ramp and the current of the drive beam, regarding the applicability of DDR injection. Furthermore, PIC simulations are utilized to find optimized conditions for the production of high-quality beams. We find and explain the intriguing result that the injection of an increased charge by means of a steepened ramp favors the generation of beams with lower emittance. Exploiting this fact enables the production of beams with high charge (∼140 pC), low normalized emittance (∼200 nm) and low uncorrelated energy spread (0.3%) in sufficiently steep ramps even for drive beams with moderate peak current (∼2.5 kA).

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Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.