Stability of exoplanetary systems retrieved from scalar time series

TL;DR

This paper introduces a new network-based method using nonlinear time series analysis to assess the stability of exoplanetary systems from scalar observational data, avoiding complex simulations.

Contribution

It presents a novel approach that transforms scalar time series into complex networks to determine system stability without n-body simulations.

Findings

Distinguishes regular and chaotic planetary dynamics

Effective on noisy and irregular observational data

Reduces computational time compared to traditional methods

Abstract

We propose a novel method applied to extrasolar planetary dynamics to describe the system stability. The observations in this field serve the measurements mainly of radial velocity, transit time, and/or celestial position. These scalar time series are used to build up the high-dimensional phase space trajectory representing the dynamical evolution of planetary motion. The framework of nonlinear time series analysis and Poincar\'e recurrences allows us to transform the obtained univariate signals into complex networks whose topology carries the dynamical properties of the underlying system. The network-based analysis is able to distinguish the regular and chaotic behaviour not only for synthetic inputs but also for noisy and irregularly sampled real world observations. The proposed scheme does not require neither n-body integration nor best fitting planetary model to perform the stability investigation, therefore, the computation time can be reduced drastically compared to those of the standard numerical methods.