Speaker
Description
For maintaining the designed optical performance of machines like the GSI heavy-ion synchrotron SIS18 or the FAIR fragment separator Super-FRS, accurate knowledge of magnetic field and element alignment is crucial. We propose a Gaussian Process model with a physics-informed kernel based on a stochastic ensemble of MAD-X lattices. This approach offers key advantages compared to LOCO (Linear Optics from Closed Orbits): (1.) Fitting Gaussian probability distributions for parameters, which inherently model uncertainty, (2.) incorporation of measurement uncertainty from BPM noise, (3.) uncertainty-enabled orbit prediction between BPMs, and (4.) an active-learning strategy for more sample efficiency than measuring a full orbit response matrix. We apply this method to two case studies aiming to reduce setup time and improve operational reliability. For SIS18, the method constructs an effective machine model with minimal orbit uncertainty around the ring, enabling orbit correction with uncertainty-quantified minimal deviation at any location. For the Super-FRS, simulated applications show how the same framework supports uncertainty-aware optics modeling in a beamline context. This physics-informed GP framework provides uncertainty-enabled modeling for rings and beamlines, with potential applications extending to broader optics-correction and beam-tuning tasks.
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