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Abstract:
We present improved methods for segmenting CO emission from galaxies into individual molecular clouds, providing an update to the cprops algorithms presented by Rosolowsky & Leroy. The new code enables both homogenization of the noise and spatial resolution among data, which allows for rigorous comparative analysis. The code also models the completeness of the data via false source injection and includes an updated segmentation approach to better deal with blended emission. These improved algorithms are implemented in a publicly available Python package, pycprops. We apply these methods to 10 of the nearest galaxies in the PHANGS-ALMA survey, cataloguing CO emission at a common 90 pc resolution and a matched noise level. We measure the properties of 4986 individual clouds identified in these targets. We investigate the scaling relations among cloud properties and the cloud mass distributions in each galaxy. The physical properties of clouds vary among galaxies, both as a function of galactocentric radius and as a function of dynamical environment. Overall, the clouds in our target galaxies are well-described by approximate energy equipartition, although clouds in stellar bars and galaxy centres show elevated line widths and virial parameters. The mass distribution of clouds in spiral arms has a typical mass scale that is 2.5× larger than interarm clouds and spiral arms clouds show slightly lower median virial parameters compared to interarm clouds (1.2 versus 1.4).
