Disk Kinematics at High Redshift: DysmalPy’s Extension to 3D Modeling and Comparison with Different Approaches
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 analy...
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Veröffentlicht in: | The Astrophysical journal 2025-01, Vol.978 (1), p.14 |
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Hauptverfasser: | , , , , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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, GalPak 3 D and 3D Barolo , 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 . |
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ISSN: | 0004-637X 1538-4357 |
DOI: | 10.3847/1538-4357/ad90b5 |