Speaker
Description
For maintaining the designed optical performance of machines like the GSI heavy-ion synchrotron SIS18 or the FAIR fragment separator SFRS, accurate knowledge of magnetic field and element alignment is crucial. A Gaussian Process model with a physics-informed kernel based on a stochastic ensemble of lattices is proposed.
Key advantages compared to LOCO (Linear Optics from Closed Orbits) include (1.) fitting of 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 greater sample efficiency than measuring an orbit response matrix. Applied in a simulation of 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. The SFRS is identified as a compelling target for future application, where sparse instrumentation and complex optics make uncertainty-aware modeling particularly valuable.
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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