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%.

Laser-wakefield accelerators (LWFAs) are high acceleration-gradient plasma-based particle accelerators capable of producing ultra-relativistic electron beams. Within the strong focusing fields of the wakefield, accelerated electrons undergo betatron oscillations, emitting a bright pulse of X-rays with a micrometer-scale source size that may be used for imaging applications. Non-destructive X-ray phase contrast imaging and tomography of heterogeneous materials can provide insight into their processing, structure, and performance. To demonstrate the imaging capability of X-rays from an LWFA we have examined an irregular eutectic in the aluminum-silicon (Al-Si) system. The lamellar spacing of the Al-Si eutectic microstructure is on the order of a few micrometers, thus requiring high spatial resolution. We present comparisons between the sharpness and spatial resolution in phase contrast images of this eutectic alloy obtained v ia X-ray phase contrast imaging at the Swiss Light Source (SLS) synchrotron and X-ray projection microscopy via an LWFA source. An upper bound on the resolving power of 2.7 ± 0.3 μ m of the LWFA source in this experiment was measured. These results indicate that betatron X-rays from laser wakefield acceleration can provide an alternative to conventional synchrotron sources for high resolution imaging of eutectics and, more broadly, complex microstructures.

We describe the use of a genetic algorithm to apply active feedback to a laser wakefield accelerator at a higher power (10 TW) and a lower repetition rate (5 Hz) than previous work. The temporal shape of the drive laser pulse was adjusted automatically to optimize the properties of the electron beam. By changing the software configuration, different properties could be improved. This included the total accelerated charge per bunch, which was doubled, and the average electron energy, which was increased from 22 to 27 MeV. Using experimental measurements directly to provide feedback allows the system to work even when the underlying acceleration mechanisms are not fully understood, and, in fact, studying the optimized pulse shape might reveal new insights into the physical processes responsible. Our work suggests that this technique, which has already been applied with low-power lasers, can be extended to work with petawattclass laser systems.

SignificanceIn the last three decades, the laser–plasma accelerator (LPA) has shown a rapid development owing to its super–high-accelerate gradients, which makes it a very promising compact accelerator and light source. Acceleration of a high-quality electron beam with divergence angle as small as possible and beam charge as high as possible has been a long-term goal ever since the inception of the LPA concept. However, until now the most popular acceleration scenario has failed to achieve both goals. We solved this problem and obtained tightly collimated electron beams with small divergence angle and extremely high beam charge (∼100 nC) via the powerful ps laser pulse interacting with a solid target.