Elimination of long-term variation from chaotic light curves

TL;DR

This paper investigates methods to remove long-term variations from chaotic light curves, demonstrating that such elimination improves the robustness of dynamical reconstructions.

Contribution

The study compares EMD and Fourier filtering techniques for removing long-term variations, enhancing the analysis of chaotic light curves.

Findings

Elimination of long-term variations improves reconstruction robustness

EMD and Fourier filtering are effective in removing disturbing variations

Long-term variations can distort dynamical analysis results

Abstract

We performed a comparative dynamical investigation of chaotic test data using the global flow reconstruction method. We demonstrate that large-amplitude, long-term variations may have a disturbing effect in the analysis. The Empirical Mode Decomposition method (EMD) and the Fourier filtering were tested to remove the additional variations. Test results show that the elimination of these variations significantly increased the robustness of the reconstructions.