449 research outputs found
Principal Component Analysis of Cavity Beam Position Monitor Signals
Model-independent analysis (MIA) methods are generally useful for analysing
complex systems in which relationships between the observables are non-trivial
and noise is present. Principle Component Analysis (PCA) is one of MIA methods
allowing to isolate components in the input data graded to their contribution
to the variability of the data. In this publication we show how the PCA can be
applied to digitised signals obtained from a cavity beam position monitor
(CBPM) system on the example of a 3-cavity test system installed at the
Accelerator Test Facility 2 (ATF2) at KEK in Japan. We demonstrate that the PCA
based method can be used to extract beam position information, and matches
conventional techniques in terms of performance, while requiring considerably
less settings and data for calibration
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