# Recurrence Network Analysis of Exoplanetary Observables

## TL;DR

This paper applies recurrence network analysis to exoplanetary data, successfully identifying different dynamical regimes in synthetic and real observations, thus providing a new approach to studying planetary dynamics.

## Contribution

It introduces a novel application of recurrence network analysis to exoplanetary data, bridging synthetic models and real observations for dynamical regime detection.

## Key findings

- Different dynamical regimes identified in synthetic data
- Analysis of real astronomical observations aligns with previous studies
- Recurrence network measures effectively characterize planetary dynamics

## Abstract

Recent advancements of complex network representation among several disciplines motivated the investigation of exoplanetary dynamics by means of recurrence networks. We are able to recover different dynamical regimes by means of various network measures obtained from synthetic time series of a model planetary system. The framework of complex networks is also applied to real astronomical observations acquired by recent state-of-the-art surveys. The outcome of the analysis is consistent with earlier studies opening new directions to investigate planetary dynamics.