分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: We present the HI distribution of galaxies from the Continuum Halos in Nearby Galaxies - an EVLA Survey (CHANG-ES). Though the observational mode was not optimized for detecting HI, we successfully produce HI cubes for 19 galaxies. The moment-0 maps from this work are available on CHANG-ES data release website, i.e., https://www.queensu.ca/changes. Our sample is dominated by star-forming, HI-rich galaxies at distances from 6.27 to 34.1 Mpc. HI interferometric images on two of these galaxies (NGC 5792 and UGC 10288) are presented here for the first time, while 12 of our remaining sample galaxies now have better HI spatial resolutions and/or sensitivities of intensity maps than those in existing publications. We characterize the average scale heights of the HI distributions for a subset of most inclined galaxies (inclination > 80 deg), and compare them to the radio continuum intensity scale heights, which have been derived in a similar way. The two types of scale heights are well correlated, with similar dependence on disk radial extension and star formation rate surface density but different dependence on mass surface density. This result indicates that the vertical distribution of the two components may be governed by similar fundamental physics but with subtle differences.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: We study the vertical distribution of the highly inclined galaxies from the Continuum Halos in Nearby Galaxies - an EVLA Survey (CHANG-ES). We explore the feasibility of photometrically deriving the HI disk scale-heights from the moment-0 images of the relatively edge-on galaxies with inclination >80 deg, by quantifying the systematic broadening effects and thus deriving correction equations for direct measurements. The corrected HI disk scale-heights of the relatively edge-on galaxies from the CHANG-ES sample show trends consistent with the quasi-equilibrium model of the vertical structure of gas disks. The procedure provide a convenient way to derive the scale-heights and can easily be applied to statistical samples in the future.