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While plasma-based accelerators have the potential to positively impact a broad range of research topics, a route to application will only be possible through improved understanding of their stability. We present experimental results of a laser wakefield accelerator in the nonlinear regime in a helium gas jet target with a density transition produced by a razor blade in the flow. Modifications to the target setup are correlated with variations in the plasma density profile diagnosed via interferometry and the shot-to-shot variations of the density profile for nominally equal conditions are characterized. Through an in-depth sensitivity study using particle-in-cell simulations, the effects of changes in the plasma density profile on the accelerated electron beams are investigated. The results suggest that blade motion is more detrimental to stability than gas pressure fluctuations, and that early focusing of the laser may reduce the deleterious effects of such density fluctuations. Published by the American Physical Society2024 

Abstract 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%.