Interplay Between Chaotic and Regular Motion in a Time-Dependent Barred Galaxy Model

TL;DR

This study investigates how the strength of the bar in a time-dependent galaxy model influences the chaotic or regular nature of star orbits, introducing a new method for efficiently distinguishing orbit types.

Contribution

It introduces a novel application of the GALI method for analyzing orbit chaos in time-dependent galactic potentials, improving detection efficiency over traditional Lyapunov exponents.

Findings

Increasing bar mass correlates with higher chaos levels.

GALI effectively detects subtle dynamical transitions.

Time-dependent models show evolving orbit behavior.

Abstract

We study the distinction and quantification of chaotic and regular motion in a time-dependent Hamiltonian barred galaxy model. Recently, a strong correlation was found between the strength of the bar and the presence of chaotic motion in this system, as models with relatively strong bars were shown to exhibit stronger chaotic behavior compared to those having a weaker bar component. Here, we attempt to further explore this connection by studying the interplay between chaotic and regular behavior of star orbits when the parameters of the model evolve in time. This happens for example when one introduces linear time dependence in the mass parameters of the model to mimic, in some general sense, the effect of self-consistent interactions of the actual N-body problem. We thus observe, in this simple time-dependent model also, that the increase of the bar's mass leads to an increase of the system's chaoticity. We propose a new way of using the Generalized Alignment Index (GALI) method as a reliable criterion to estimate the relative fraction of chaotic vs. regular orbits in such time-dependent potentials, which proves to be much more efficient than the computation of Lyapunov exponents. In particular, GALI is able to capture subtle changes in the nature of an orbit (or ensemble of orbits) even for relatively small time intervals, which makes it ideal for detecting dynamical transitions in time-dependent systems.