Speaker
Description
Heavy ion synchrotrons, like the SIS18 at GSI, rely on the proven numerical approaches to correct the closed orbit. The SIS18 has a relative moderate amount of BPMs (one per cell) and requires a well corrected and known orbit, especially near the injection/extraction systems. Fluctuations of the BPM signal arise from the electronics. In addition there are systematic errors due to the relative positioning of the BPMs. At specific locations, like the the beam position at the extraction septum, it is desirable to have a prediction, including an uncertainty estimate. An adapted approach towards closed orbit correction is proposed that integrates probabilistic modeling with beam dynamics to infer a closed orbit including uncertainty quantification. Methods, such as LOCO (Linear Optics from Closed Orbits) and NOECO (Nonlinear Optics from Off-Energy Closed Orbits), are limited by the need for extensive orbit response matrix (ORM) measurements and lack uncertainty quantification. The proposed method leverages physics-informed Bayesian regression to develop a surrogate model that not only quantifies uncertainties at beam position monitors (BPMs) but also in between them, reducing the required data. A Gaussian Process (GP) model is used to incorporate beam dynamics by estimating the kernel (and mean function) through the evaluation of simulated realizations, with simulations based on a MAD-X model of the SIS18 lattice. The learned distribution of multipole misalignments enables a model of the closed orbit with integrated uncertainty and noise handling. This model is then used in a Bayesian optimization framework to correct the closed orbit and achieve minimal deviation at specific locations, such as at the septum. The approach has also broader applications towards more general optics corrections.
