Disk Kinematics at High Redshift: DysmalPy’s Extension to 3D Modeling and Comparison with Different Approaches

• Published in: The Astrophysical journal 2025-01, Vol.978 (1), p.14
• Main Authors: Lee, Lilian L., Förster Schreiber, Natascha M., Price, Sedona H., Liu, Daizhong, Genzel, Reinhard, Davies, Ric, Tacconi, Linda J., Shimizu, Taro T., Nestor Shachar, Amit, Espejo Salcedo, Juan M., Pastras, Stavros, Wuyts, Stijn, Lutz, Dieter, Renzini, Alvio, Übler, Hannah, Herrera-Camus, Rodrigo, Sternberg, Amiel
• Format: Article
• Language: English
• Subjects: Astronomical models; Astronomy data analysis; Astronomy data modeling; Dispersion; Emission analysis; Galactic rotation; Galaxies; Galaxy dynamics; Galaxy kinematics; High-redshift galaxies; Infrared analysis; Infrared imaging; Interferometry; Kinematics; Near infrared radiation; Parameter sensitivity; Red shift; Rotating disks; Signal to noise ratio; Spectroscopy; Star formation; Stars & galaxies; Velocity
• ISSN: 0004-637X; 1538-4357
• DOI: 10.3847/1538-4357/ad90b5

Spatially resolved emission-line kinematics are invaluable for investigating fundamental galaxy properties and have become increasingly accessible for galaxies at z ≳0.5 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 the lower signal-to-noise ratio (S/N) and resolution compared to the data of local galaxies. We present and test the 3D fitting functionality of DysmalPy, examining how well it recovers the 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 ∼ 1−3 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 nonparametric tool can lead to differences of up to a factor of 2. Our tests highlight and the importance of deep, high-resolution data and the need for careful consideration of (i) the choice of priors (parametric approaches); and (ii) the masking (all approaches); and (iii), more generally, the evaluating of the suitability of each approach to the specific data at hand. This paper accompanies the public release of DysmalPy.