Speaker
Description
Turn-by-turn (TbT) data are readily available in modern circular accelerators and are widely used to infer machine parameters in both simulations and experiments. In the latter case, TbT data record transverse beam-centroid positions from beam position monitors (BPMs) and therefore include measurement noise and decoherence. We construct high-dimensional time-delay embedding of TbT time series, yielding matrix representations of the signals. Since the signals considered are typically near-quasiperiodic with harmonics of the fundamental betatron frequencies, the embedded matrices are expected to be low-rank. We leverage this rank structure to define a complexity indicator on singular-value spectra, which are related to the underlying quasiperiodic structure of the TbT signals.The proposed framework provides a simple, data-driven diagnostic for complexity estimation directly from TbT records and is compatible with experimental datasets.
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