Identification of molecular clouds in emission maps: a comparison between methods in the 13CO/C18O (J = 3–2) Heterodyne Inner Milky Way Plane Survey

Abstract

The growing range of automated algorithms for the identification of molecular clouds and clumps in large observational datasets has prompted the need for the direct comparison of these procedures. However, these methods are complex and testing for biases is often problematic: only a few of them have been applied to the same data set or calibrated against a common standard. We compare the Fellwalker method, a widely used watershed algorithm, to the more recent Spectral Clustering for Interstellar Molecular Emission Segmentation (SCIMES). SCIMES overcomes sensitivity and resolution biases that plague many friends-of-friends algorithms by recasting cloud segmentation as a clustering problem. Considering the 13CO/C18O (J = 3 − 2) Heterodyne Inner Milky Way Plane Survey (CHIMPS) and the CO High-Resolution Survey (COHRS), we investigate how these two different approaches influence the final cloud decomposition. Although the two methods produce largely similar statistical results over the CHIMPS dataset, FW appears prone to over-segmentation, especially in crowded fields where gas envelopes around dense cores are identified as adjacent, distinct objects. FW catalogue also includes a number of fragmented clouds that appear as different objects in a line-of-sight projection. In addition, cross-correlating the physical properties of individual sources between catalogues is complicated by different definitions, numerical implementations, and design choices within each method, which make it very difficult to establish a one-to-one correspondence between the sources.

Relative Velocities between 13CO Structures within 12CO Molecular Clouds

“…This includes their masses, sizes, velocity dispersion, etc., and the scaling relations between cloud sizes, velocity dispersion, surface densities, etc. (e.g., Larson 1981; Dame et al 1986;Solomon et al 1987;Heyer et al 2009;Roman-Duval et al 2010;Rice et al 2016;Miville-Deschênes et al 2017;Riener et al 2020;Rani et al 2023). Moreover, the column density probability distribution (e.g., Vazquez-Semadeni 1994;Ma et al 2021Ma et al , 2022 and the velocity structure function (e.g., Heyer & Brunt 2004;Heyer et al 2006) are also used to investigate the distributions of column densities and velocity fields in MCs.…”

A Catalog of 13CO Clumps from the MWISP in l = 10°–20°

“…With the progress of sky survey projects, unsupervised machine learning-based algorithms for detecting molecular clumps have also emerged (e.g., Rosolowsky et al 2008;Berry 2015;Luo et al 2022;Jiang et al 2023). The dendrogram algorithm, developed by Rosolowsky et al (2008), is well-suited for illustrating changes in the hierarchical structure of isosurfaces within molecular line data cubes as contour levels vary (Rani et al 2023). It has been widely used in continuum, atomic hydrogen, and molecular line data (Cheng et al 2018;Takekoshi et al 2019;Nakanishi et al 2020;Zhang et al 2021).…”