How does noise affect the structure of a chaotic attractor: A recurrence network perspective

TL;DR

This paper investigates how white and colored noise influence the topology of chaotic attractors using recurrence networks, demonstrating the method's effectiveness on both simulated and real-world data from a black hole system.

Contribution

It introduces a recurrence network approach to analyze the impact of noise on chaotic attractors and applies it to real astrophysical data.

Findings

Noise destroys recurrence in phase space

Recurrence network measures identify noise type in data

Method effective on real-world black hole light curves

Abstract

We undertake a preliminary numerical investigation to understand how the addition of white and colored noise to a time series affects the topology and structure of the underlying chaotic attractor. We use the methods and measures of recurrence networks generated from the time series for this analysis. We explicitly show that the addition of noise destroys the recurrence of trajectory points in the phase space. By using the results obtained from this analysis, we go on to analyse the light curves from a dominant black hole system and show that the recurrence network measures are effective in the analysis of real world data involving noise and are capable of identifying the nature of noise contamination in a time series.