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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%. 

Hydrodynamical cosmological simulations have recently made great advances in reproducing galaxy mass assembly over cosmic time – as often quantified from the comparison of their predicted stellar mass functions to observed stellar mass functions from data. In this paper, we compare the clustering of galaxies from the hydrodynamical cosmological simulated light-cone Horizon-AGN to clustering measurements from the VIDEO survey observations. Using mocks built from a VIDEO-like photometry, we first explore the bias introduced into clustering measurements by using stellar masses and redshifts derived from spectral energy distribution fitting, rather than the intrinsic values. The propagation of redshift and mass statistical and systematic uncertainties in the clustering measurements causes us to underestimate the clustering amplitude. We then find that clustering and halo occupation distribution (HOD) modelling results are qualitatively similar in Horizon-AGN and VIDEO. However, at low stellar masses, Horizon-AGN underestimates the observed clustering by up to a factor of ∼3, reflecting the known excess stellar mass to halo mass ratio for Horizon-AGN low-mass haloes, already discussed in previous works. This reinforces the need for stronger regulation of star formation in low-mass haloes in the simulation. Finally, the comparison of the stellar mass to halo mass ratio in the simulated catalogue, inferred from angular clustering, to that directly measured from the simulation validates HOD modelling of clustering as a probe of the galaxy–halo connection.