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

Raffaele Rani*, Toby J.T. Moore, David J. Eden, Andrew J. Rigby, Ana Duarte-Cabral, Yueh Ning Lee

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

2 引文 斯高帕斯(Scopus)

摘要

The growing range of automated algorithms for the identification of molecular clouds and clumps in large observational data sets 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 oversegmentation, 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.

原文英語
頁(從 - 到)1832-1852
頁數21
期刊Monthly Notices of the Royal Astronomical Society
523
發行號2
DOIs
出版狀態已發佈 - 2023 8月 1

ASJC Scopus subject areas

  • 天文和天體物理學
  • 空間與行星科學

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