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RIS citation export for THXBA2: Analysis of Beam Position Monitor Requirements with Bayesian Gaussian Regression

TY  - CONF
AU  - Li, Y.
AU  - Cheng, W.X.
AU  - Hao, Y.
AU  - Rainer, R.S.
ED  - Yamazaki, Yoshishige
ED  - Raubenheimer, Tor
ED  - McCausey, Amy
ED  - Schaa, Volker RW
TI  - Analysis of Beam Position Monitor Requirements with Bayesian Gaussian Regression
J2  - Proc. of NAPAC2019, Lansing, MI, USA, 01-06 September 2019
CY  - Lansing, MI, USA
T2  - North American Particle Accelerator Conference
T3  - 4
LA  - english
AB  - 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.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 912
EP  - 915
KW  - optics
KW  - undulator
KW  - brightness
KW  - quadrupole
KW  - emittance
DA  - 2019/10
PY  - 2019
SN  - 2673-7000
SN  - 978-3-95450-223-3
DO  - doi:10.18429/JACoW-NAPAC2019-THXBA2
UR  - http://jacow.org/napac2019/papers/thxba2.pdf
ER  -