IPAC2019 - List of Authors (Rainer, R.S.)

Paper	Title	Page
MOPGW114	Bayesian Approach for Linear Optics Correction	390
	Y. Li, W.X. Cheng, R.S. Rainer BNL, Upton, Long Island, New York, USA
	With a Bayesian approach, the linear optics correction algorithm for storage rings is revisited. In modern ring-based accelerators, optics corrections are determined from repetitive measurements which help identify systematic and random quadrupole errors in presence of various measurement noises. This process is a multivariate nonlinear regression problem driven by either a completed lattice model or a Jacobian matrix. Starting from the Bayes’ theorem, ’likelihood functions’ and ’prior probability’ distributions are extracted from a complete linear optics model. Under some assumptions, the least square algorithm and then the Jacobian matrix approach can be re-derived. The coherence of the correction algorithm is ensured through specifying a self-consistent regularization coefficient to prevent overfitting. Optimal weights for different correction objectives are obtained based on their measurement noise level. A new technique has been developed to resolve degenerated quadrupole errors when observed at a few select BPMs. A necessary condition of being distinguishable is that their optics response vectors seen at these specific BPMs should be near-orthogonal.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-MOPGW114
About •	paper received ※ 18 April 2019 paper accepted ※ 20 May 2019 issue date ※ 21 June 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Bayesian Approach for Linear Optics Correction

390

Y. Li, W.X. Cheng, R.S. Rainer
BNL, Upton, Long Island, New York, USA

With a Bayesian approach, the linear optics correction algorithm for storage rings is revisited. In modern ring-based accelerators, optics corrections are determined from repetitive measurements which help identify systematic and random quadrupole errors in presence of various measurement noises. This process is a multivariate nonlinear regression problem driven by either a completed lattice model or a Jacobian matrix. Starting from the Bayes’ theorem, ’likelihood functions’ and ’prior probability’ distributions are extracted from a complete linear optics model. Under some assumptions, the least square algorithm and then the Jacobian matrix approach can be re-derived. The coherence of the correction algorithm is ensured through specifying a self-consistent regularization coefficient to prevent overfitting. Optimal weights for different correction objectives are obtained based on their measurement noise level. A new technique has been developed to resolve degenerated quadrupole errors when observed at a few select BPMs. A necessary condition of being distinguishable is that their optics response vectors seen at these specific BPMs should be near-orthogonal.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2019-MOPGW114

About •

paper received ※ 18 April 2019 paper accepted ※ 20 May 2019 issue date ※ 21 June 2019

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)