Time-varying Bayesian optimisation for continual optimal injection in the CERN PS Booster

The Proton Synchrotron Booster (PSB) receives 160 MeV H- ions, which are converted to protons at injection via a charge exchange mechanism, an upgrade that allows the production of low-loss high-intensity beams (> 10^13 per ring). To mitigate losses due to space charge, horizontal phase-space painting is performed with a system of fours kickers whose pulse is customisable via time and amplitude parameters. Recent work has shown that classical optimisation algorithms can find the optimal parameter values on both a digital twin and the real machine. However, these techniques: do not handle system-state time variations, do not continually update the parameters during operation, require non-negligible dedicated beam time and are usually not robust to observation noise. We suggest time-varying Bayesian optimisation and show that it addresses each of the previous issues at low development and deployment cost. This work improves the operation of the PSB and contributes towards the goal of automating the operation of particle accelerators.