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GLOBAL Hɪ PROPERTIES OF GALAXIES VIA SUPER-PROFILE ANALYSIS

  • Kim, Minsu (Department of Astronomy and Space Science, Sejong University) ;
  • Oh, Se-Heon (Department of Astronomy and Space Science, Sejong University)
  • Received : 2022.06.30
  • Accepted : 2022.08.29
  • Published : 2022.10.31

Abstract

We present a new method which constructs an Hɪ super-profile of a galaxy which is based on profile decomposition analysis. The decomposed velocity profiles of an Hɪ data cube with an optimal number of Gaussian components are co-added after being aligned in velocity with respect to their centroid velocities. This is compared to the previous approach where no prior profile decomposition is made for the velocity profiles being stacked. The S/N improved super-profile is useful for deriving the galaxy's global Hɪ properties like velocity dispersion and mass from observations which do not provide sufficient surface brightness sensitivity for the galaxy. As a practical test, we apply our new method to 64 high-resolution Hɪ data cubes of nearby galaxies in the local Universe which are taken from THINGS and LITTLE THINGS. In addition, we also construct two additional Hɪ super-profiles of the sample galaxies using symmetric and all velocity profiles of the cubes whose centroid velocities are determined from Hermite h3 polynomial fitting, respectively. We find that the Hɪ super-profiles constructed using the new method have narrower cores and broader wings in shape than the other two super-profiles. This is mainly due to the effect of either asymmetric velocity profiles' central velocity bias or the removal of asymmetric velocity profiles in the previous methods on the resulting Hɪ super-profiles. We discuss how the shapes (𝜎n/𝜎b, An/Ab, and An/Atot) of the new Hɪ super-profiles which are measured from a double Gaussian fit are correlated with star formation rates of the sample galaxies and are compared with those of the other two super-profiles.

Keywords

Acknowledgement

S.-H. Oh acknowledges a support from the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT: MSIT) (No. NRF-2020R1A2C1008706).

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