1–6 Jun 2025
Taipei International Convention Center (TICC)
Asia/Taipei timezone

A physics-informed Gaussian Process to model the closed orbit in a synchrotron

THPM017
5 Jun 2025, 15:30
2h
Exhibiton Hall A _Magpie (TWTC)

Exhibiton Hall A _Magpie

TWTC

Poster Presentation MC6.D13 Machine Learning Thursday Poster Session

Speaker

Adrian Oeftiger (GSI Helmholtz Centre for Heavy Ion Research)

Description

To construct a closed orbit model for an accelerator ring with intrinsic uncertainty quantification from orbit measurements, a physics-informed Gaussian Process model is proposed based on a stochastic ensemble of MAD-X lattices. Key advantages compared to LOCO (Linear Optics from Closed Orbits) include (1.) uncertainty-enabled orbit prediction in between BPMs (beam position monitors), (2.) fitting of a parameter distribution (dipole-like field errors) which inherently models uncertainty, (3.) incorporation of measurement uncertainty from BPM noise, and (4.) an active learning approach which can be more sample efficient than measuring an orbit response matrix. A case study is presented for the GSI heavy ion synchrotron SIS18 with various simulated applications, in particular constructing an effective machine model with minimal orbit uncertainty around the ring, and orbit correction to achieve minimal deviation at a specific location such as, e.g., the septum to control beam loss during slow extraction. This physics-inspired Gaussian Process regression approach shows potential to be applied to optics correction and further applications beyond closed orbit correction.

Region represented Europe
Paper preparation format LaTeX

Author

Adrian Oeftiger (GSI Helmholtz Centre for Heavy Ion Research)

Co-authors

Oliver Boine-Frankenheim (Technical University of Darmstadt) Victoria Isensee (Technical University of Darmstadt)

Presentation materials

There are no materials yet.