Speaker
Description
Inferring physical parameters — such as magnetic field and alignment errors -- from sparse, noisy sensor data is essential for maintaining designed performance in machines like the GSI heavy-ion synchrotron SIS18 and the FAIR fragment separator SFRS. Rather than fitting a surrogate to simulation output, we construct a Gaussian Process kernel from an ensemble of forward simulations of the machine lattice, retaining physical fidelity while enabling GP-style uncertainty quantification.
Compared to LOCO (Linear Optics from Closed Orbits), the accelerator-physics standard, we propose this as a probabilistic alternative offering: (1) parameter estimates with quantified uncertainty rather than point fits, (2) principled incorporation of measurement noise, (3) uncertainty-quantified interpolation between sensors, and (4) an active-learning strategy for improved sample efficiency.
Demonstrated on a simulated SIS18, the method substantially reduces orbit uncertainty around the ring using a batch-selection strategy for measurement locations. We identify the SFRS, with sparse instrumentation and complex optics, as a promising target application, and discuss open challenges in scaling the approach to more complex beamlines.