Speaker
Description
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 high-intensity beams ($> 10^{13}$ per ring). Nevertheless, with the increase in intensity, space-charge losses arise. To mitigate these effects, 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 performs well, addressing each of the previous issues. Moreover, it is simple to develop and deploy (i.e. cheap in human capital), and runs continually, with minimal human interaction. We also discuss context-based variants of this algorithm. This work improves the operation of the PSB and contributes towards the goal of automating the operation of particle accelerators.
