Bar Properties and Star-by-Star Bar Membership via Action Conservation

TL;DR

This paper introduces a new star-by-star dynamical action-based method to identify and characterize galactic bars, leveraging Gaia data for a consistent classification approach.

Contribution

It develops a rigorous, action conservation-based classification scheme for galactic bars, enabling individual star membership determination and bar parameter estimation.

Findings

Bar regions show fewer stars with conserved actions.

Stars on bar orbits exhibit larger action changes.

Method accurately recovers bar length and orientation.

Abstract

A bar-like central feature is commonly observed in both nearby and distant spiral-type galaxies, including the Milky Way. While many methods exist to categorise this morphology, no one method has emerged as the field-wide standard. To develop a rigorous and consistent method for identifying these bars, we investigate a classification scheme based on dynamical actions. In the Gaia era, actions can be estimated for individual stars in both observations and simulations, making this a natural and unifying diagnostic, assuming the intrinsic errors and selection functions are understood. Our approach is straightforward: stars that participate in the bar are subject to a strongly non-axisymmetric potential and, therefore, do not completely conserve their actions. We use this property to define a star-by-star criterion, formulated as an inequality and evaluated within measurement uncertainties, to identify bar members based on the degree to which their total action fails to be conserved. From tests on simulated galaxies, we find that the bar region is indeed characterised by a lower fraction of stars with conserved actions and that stars on bar orbits are represented by larger percentage changes in their actions. We are able to classify the spatial extent of barred region via the standard parameters of bar length and orientation, while also individually separating bar-located from bar-member stars on bar orbits. As proof of concept, our automated method based on dynamical actions robustly identifies bar parameters that closely match the eye's performance (average bar length variation ~9%) in barred snapshots of the test galaxy.