Recovering the pattern speeds of edge-on barred galaxies via an orbit-superposition method

TL;DR

This paper introduces an orbit-superposition method to accurately recover the pattern speeds of edge-on barred galaxies, demonstrating its effectiveness through simulated data with varying bar orientations and morphologies.

Contribution

The paper presents a novel orbit-superposition technique specifically designed for edge-on barred galaxies, enabling reliable pattern speed measurements across different bar angles and morphologies.

Findings

Model-recovered pattern speeds match true speeds within 1σ uncertainties in most cases.

Uncertainties depend on bar azimuthal angles, with stricter morphology constraints reducing errors.

2σ confidence levels always encompass the true pattern speeds.

Abstract

We developed an orbit-superposition method for edge-on barred galaxies and evaluated its capability to recover the bar pattern speed $\rm\Omega_p$. We selected three simulated galaxies (Au-18, Au-23, and Au-28) with known pattern speeds from the Auriga simulations and created MUSE-like mock data sets with edge-on views (inclination angles $\theta_{\rm T}\ge85^\circ$) and various bar azimuthal angles $\varphi_{\rm T}$. For mock data sets with side-on bars ($\varphi_{\rm T}\ge50^\circ$), the model-recovered pattern speeds $\rm\Omega_p$ encompass the true pattern speeds $\rm\Omega_T$ within the model uncertainties ($1\sigma$ confidence levels, $68\%$) for 10 of 12 cases. The average model uncertainty within the $1\sigma$ confidence levels is equal to $10\%$. For mock data sets with end-on bars ($\varphi_{\rm T}\le30^\circ$), the model uncertainties of $\rm\Omega_p$ depend significantly on the bar azimuthal angles $\varphi_{\rm T}$, with the uncertainties of cases with $\varphi_{\rm T}=10^\circ$ approaching $\sim30\%$. However, by imposing a stricter constraint on the bar morphology ($p_{\rm bar}\le0.50$), the average uncertainties are reduced to $14\%$ , and $\rm\Omega_p$ still encompass $\rm\Omega_T$ within the model uncertainties for three of four cases. For all the models that we create in this paper, the $2\sigma$ ($95\%$) confidence levels of the model-recovered pattern speeds $\rm\Omega_p$ always cover the true values $\rm\Omega_T$.