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Recurrence Analysis as a tool to study chaotic dynamics of extreme mass ratio inspiral in signal with noise
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Recurrence analysis is a well settled method allowing to discern chaos from order, and determinism from noise. We apply this tool to study time series representing geodesic and inspiraling motion of a test particle in a deformed Kerr spacetime, when deterministic chaos and different levels of stochastic noise are present. In particular, we suggest a recurrence-based criterion to reveal whether the time series comes from a deterministic source and find a noise-level threshold of its applicability.
Forward citations
Cited by 2 Pith papers
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Combining Machine Learning with Recurrence Analysis for resonance detection
A machine learning model trained on recurrence quantifiers of a standard map can detect resonances in other 2D systems, but its generalization to a 4D map requires matching embedding conditions and yields unclear peaks.
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Chaotic motion of particles around a Schwarzschild black hole in a swirling electromagnetic background
Numerical chaos indicators applied to the Schwarzschild-Bertotti-Robinson-Bonnor-Melvin family show that chaos occurs without swirling and that electromagnetic field strengths and directions tightly restrict bound orbits.
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