Kinematic Decomposition of the H i Gaseous Component in the Large Magellanic Cloud

Abstract

We perform a profile analysis of the combined H i data cube of the Large Magellanic Cloud (LMC) from observations with the Australia Telescope Compact Array and the Parkes radio telescope. For the profile analysis, we use a newly developed algorithm that decomposes individual line profiles into an optimal number of Gaussian components based on a Bayesian nested sampling. The decomposed Gaussian components are then classified into kinematically cold, warm, and hot gas components based on their velocity dispersion. The estimated masses of the kinematically cold, warm, and hot gas components are ∼12.2%, ∼58.3%, and ∼29.5% of the total H i mass of the LMC, respectively. Our analysis reveals the highly complex H i structure and kinematics of the LMC that are seen in previous studies but in a more quantitative manner. We also extract the undisturbed H i gas bulk motions and derive new H i gas bulk rotation curves of the LMC by applying a 2D tilted-ring analysis. In contrast to previously derived H i rotation curves, the newly derived bulk rotation curves are much more consistent with the carbon star kinematics, with rotation velocity linearly increasing in the inner part and reaching a maximum of ∼60 km s−1 at the outermost measured radius. By comparing the lower bulk rotation curves with previous studies, we conclude that there is a lower dynamical contribution of dark matter in the central part of the LMC.

Gas Dynamics and Star Formation in NGC 6822

“…We first decompose all the line profiles of the ATCA H I data cube with optimal numbers of Gaussian components using a tool, the so-called BAYGAUD (Oh et al 2019(Oh et al , 2022, which is based on a Bayesian analysis technique. After convolving the raw data cube using a 2D Gaussian kernel to make its spatial resolution 48 0 × 48 0, we resample the cube with a pixel scale of 48 0 pixel −1 , which corresponds to a physical scale of ∼114 pc.…”

“…To improve the parameter estimation and the model selection in the profile analysis, we make a practical application of a newly developed profile decomposition al-gorithm, the so-called baygaud 7 (Oh et al 2019;Oh et al 2022). baygaud which is based on a Bayesian analysis technique allows us to decompose a line profile into an optimal number of Gaussian components, circumventing the limited capability of the conventional profile analysis, particularly for non-Gaussian line profiles.…”

WALLABY Pilot Survey: H i gas kinematics of galaxy pairs in cluster environment

“…From the performance test discussed in Oh et al (2019a), is found to be robust for parameter estimation and model selection against any local minima in the course of the fitting as long as the integrated signal-to-noise (S/N) of the line profile is high enough for the analysis, for example, larger than three or so. We refer to Oh et al (2019a) for the full description of the fitting algorithm and its performance test (see also Oh et al 2022 andPark et al 2022).…”

“…We note that these single Gaussian fitting velocity fields which are based on the line profile fitting analysis are less affected by any spike-like noise in the line profiles, and thus better estimate their representative centroid velocities than the conventional moment analysis ( 1) in most cases. However, the single Gaussian fitting method has limited success in modelling non-Gaussian profile shapes (e.g., Oh et al 2022). Alternative fitting forms, on the other hand, such as the Hermite polynomial and multiple Gaussian functions have worked in other cases (de Blok et al 2008;Oh et al 2008Oh et al , 2011Oh et al , 2015Oh et al , 2022.…”

“…We perform profile decomposition of H velocity profiles using a new tool, . This new software allows us to de-blend a line-of-sight velocity profile with an optimal number of Gaussian components based on Bayesian analysis techniques (Oh et al 2019a;Oh et al 2022;Park et al 2022). We construct an H super-profile of each galaxy via stacking of individual line profiles after aligning their central velocities.…”