Recurrence Analysis as a tool to study chaotic dynamics of extreme mass ratio inspiral in signal with noise

TL;DR

This paper applies recurrence analysis to distinguish chaos from noise in the dynamics of particles around deformed Kerr black holes, providing criteria to identify deterministic signals amidst stochastic noise.

Contribution

It introduces a recurrence-based criterion to detect determinism and noise thresholds in complex astrophysical time series involving chaotic dynamics.

Findings

Recurrence analysis effectively differentiates chaos from noise in simulated signals.

A noise-level threshold for the method's applicability is established.

The approach aids in analyzing signals from extreme mass ratio inspirals in noisy environments.

Abstract

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.