Method of Running Sines: Modeling Variability in Long-Period Variables
Ivan L. Andronov, Lidia L. Chinarova

TL;DR
The paper reviews the 'Running Sines' method for analyzing nearly periodic signals with variable periods, demonstrating its effectiveness on astrophysical data like long-period variables and binary star oscillations.
Contribution
It introduces and evaluates the 'Running Sines' method as a tool for modeling variability in irregularly spaced time series, especially in astrophysical contexts.
Findings
Effective for signals with large period variations
Applicable to irregularly spaced data
Successfully applied to various variable stars
Abstract
We review one of complementary methods for time series analysis - the method of "Running Sines". "Crash tests" of the method include signals with a large period variation and with a large trend. The method is most effective for "nearly periodic" signals, which exhibit "wavy shape" with a "cycle length" varying within few dozen per cent (i.e. oscillations of low coherence). This is a typical case for brightness variations of long-period pulsating variables and resembles QPO (Quasi-Periodic Oscillations) and TPO (Transient Periodic Oscillations) in interacting binary stars - cataclysmic variables, symbiotic variables, low-mass X-Ray binaries etc. General theory of "running approximations" was described by Andronov (1997A &AS..125..207A), one of realizations of which is the method of "running sines". The method is related to Morlet-type wavelet analysis improved for irregularly spaced data…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Astrophysical Phenomena and Observations · Astronomy and Astrophysical Research
