Speaker
Description
Tuning injectors is a challenging task for the operation of accelerator facilities and synchrotron light sources,
particularly during the commissioning phase. Efficient tuning of the transfer line is essential for ensuring
optimal beam transport and injection efficiency. This process is further complicated by challenges such as
beam misalignment in quadrupole magnets, which can degrade beam quality and disrupt operations. Traditional
tuning methods are often time-consuming and insufficient for addressing the complexities of highdimensional
parameter spaces.
In this work, we explore the use of advanced AI methods, including Bayesian optimization, to automate and
improve the tuning process. Initial results, demonstrated on the transfer line of KARA (Karlsruhe Research
Accelerator) at KIT (Karlsruhe Institute of Technology), show promising improvements in beam alignment
and transport efficiency, representing first steps toward more efficient and reliable accelerator operation.
This study is part of the RF2.0 project, funded by the Horizon Europe program of the European Commission,
which focuses on advancing energy-efficient solutions for particle accelerators.
