|THXBA2||Analysis of Beam Position Monitor Requirements with Bayesian Gaussian Regression||912|
|WEPLM09||use link to see paper's listing under its alternate paper code|
Funding: This research is supported by U.S. Department of Energy under Contract No. DE-SC0012704, and the NSF under Cooperative Agreement PHY-1102511.
With a Bayesian Gaussian regression approach, a systematic method for analyzing a storage ring’s beam position monitor (BPM) system requirements has been developed. The ultimate performance of a ring-based accelerator, based on brightness or luminosity, is determined not only by global parameters, but also by local beam properties at some particular points of interest (POI). BPMs used for monitoring the beam properties, however, can not be located at these points. Therefore, the underlying and fundamental purpose of a BPM system is to predict whether the beam properties at POIs reach their desired values. The prediction process is a regression problem with BPM readings as the training data, but containing random noise. A Bayesian Gaussian regression approach can determine the probability distribution of the predictive errors, which can be used to conversely analyze the BPM system requirements. This approach is demonstrated by using turn-by-turn data to reconstruct a linear optics model, and predict the brightness degradation for a ring-based light source. The quality of BPMs was found to be more important than their quantity in mitigating predictive errors.
|Slides THXBA2 [3.205 MB]|
|Poster THXBA2 [7.083 MB]|
|DOI •||reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-THXBA2|
|About •||paper received ※ 16 August 2019 paper accepted ※ 04 September 2019 issue date ※ 08 October 2019|
|Export •||reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)|