The Strength of Bisymmetric Modes in SDSS-IV/MaNGA Barred Galaxy Kinematics

TL;DR

This paper introduces Nirvana, a Bayesian modeling code that decomposes galaxy velocity fields into non-axisymmetric modes, enabling detailed analysis of barred galaxy kinematics using MaNGA data.

Contribution

We develop and validate Nirvana, a minimally-supervised Bayesian code for decomposing galaxy velocity fields into radial and tangential modes, applied to a large MaNGA barred galaxy sample.

Findings

Nirvana detects elevated non-circular motions in barred galaxies.

Bar position angles from Nirvana agree with visual classifications.

The study provides a large statistical sample for analyzing non-axisymmetric kinematics.

Abstract

The SDSS-IV/MaNGA Survey data provide an unprecedented opportunity to study the internal motions of galaxies and, in particular, represent the largest sample of barred galaxy kinematic maps obtained to date. We present results from Nirvana, our non-axisymmetric kinematic modeling code built with a physically-motivated Bayesian forward modeling approach, which decomposes MaNGA velocity fields into first- and second-order radial and tangential rotational modes in a generalized and minimally-supervised fashion. We use Nirvana to produce models and rotation curves for 1263 unique barred MaNGA galaxies and a matched unbarred control sample We present our modeling approach, tests of its efficacy, and validation against existing visual bar classifications. Nirvana finds elevated non-circular motions in galaxies identified as bars in imaging, and bar position angles that agree well with visual measurements. The Nirvana-MaNGA barred and control samples provide a new opportunity for studying the influence of non-axisymmetric internal disk kinematics in a large statistical sample.