Disk kinematics at high redshift: DysmalPy's extension to 3D modeling and comparison with different approaches

TL;DR

This paper evaluates DysmalPy's 3D kinematic modeling capabilities for high-redshift galaxies, comparing its performance with other tools and emphasizing data quality and methodological considerations.

Contribution

It introduces and tests DysmalPy's 3D fitting functionality, benchmarking it against GalPak3D and 3DBarolo for high-z galaxy kinematic analysis.

Findings

All tools accurately recover rotation velocity.

Velocity dispersion recovery depends on S/N and methodology.

Methodological differences can cause up to twofold variation.

Abstract

Spatially-resolved emission line kinematics are invaluable to investigating fundamental galaxy properties and have become increasingly accessible for galaxies at $z\gtrsim0.5$ through sensitive near-infrared imaging spectroscopy and millimeter interferometry. Kinematic modeling is at the core of the analysis and interpretation of such data sets, which at high-z present challenges due to lower signal-to-noise ratio (S/N) and resolution compared to data of local galaxies. We present and test the 3D fitting functionality of DysmalPy, examining how well it recovers intrinsic disk rotation velocity and velocity dispersion, using a large suite of axisymmetric models, covering a range of galaxy properties and observational parameters typical of $z\sim1$-$3$ star-forming galaxies. We also compare DysmalPy's recovery performance to that of two other commonly used codes, GalPak3D and 3DBarolo, which we use in turn to create additional sets of models to benchmark DysmalPy. Over the ranges of S/N, resolution, mass, and velocity dispersion explored, the rotation velocity is accurately recovered by all tools. The velocity dispersion is recovered well at high S/N, but the impact of methodology differences is more apparent. In particular, template differences for parametric tools and S/N sensitivity for the non-parametric tool can lead to differences up to a factor of 2. Our tests highlight and the importance of deep, high-resolution data and the need for careful consideration of: (1) the choice of priors (parametric approaches), (2) the masking (all approaches) and, more generally, evaluating the suitability of each approach to the specific data at hand. This paper accompanies the public release of DysmalPy.