Tune identification - i.e. attribution of the spectral peak to a particular normal mode of oscillations - can present a significant difficulty in the presence of strong transverse coupling when the normal mode with a lower damping rate dominates spectra of Turn-by-Turn oscillations in both planes. The introduced earlier phased sum algorithm helped to recover the weaker normal mode signal from the noise, but by itself proved to be insufficient for automatic peak identification in the case of close phase advance distribution in both planes. To resolve this difficulty we modified the algorithm by taking and analyzing Turn-by-Turn data for two different ramps with the beam oscillation excited in each plane in turn. Comparison of the relative amplitudes of Fourier components allows for automatic correct tune identification. The proposed algorithm was implemented in the Fermilab Booster B38 console application and successfully used in tune, coupling and chromaticity measurements.
Real-Time-Capable Betatron Tune Measurement from Schottky Spectra Using Deep Learning and Uncertainty-Aware Kalman Filtering
Shanghai Institute of Applied Physics, Chinese Academy of Sciences（中国科学院上海应用物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海先进研究院）；Northwest Institute of Nuclear Technology (NINT)（中国核动力研究设计院）
Robust Betatron-Tune Measurement from Schottky Spectra: Complementary Classical and Deep-Learning Paradigms
Shanghai Institute of Applied Physics, Chinese Academy of Sciences（中国科学院上海应用物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海高级研究院）
