English

Multi log-normal density structure in Cygnus-X molecular clouds: A fitting for N-PDF without power-law

Astrophysics of Galaxies 2023-05-31 v1

Abstract

We studied the H2_2 column density probability distribution function (N-PDF) based on molecular emission lines using the Nobeyama 45-m Cygnus X CO survey data. Using the DENDROGRAM and SCIMES algorithms, we identified 124 molecular clouds in the 13^{13}CO data. From these identified molecular clouds, an N-PDF was constructed for 11 molecular clouds with an extent of more than 0.4 deg2^2. From the fitting of the N-PDF, we found that the N-PDF could be well-fitted with one or two log-normal distributions. These fitting results provided an alternative density structure for molecular clouds from a conventional picture. We investigated the column density, dense molecular cloud cores, and radio continuum source distributions in each cloud and found that the N-PDF shape was less correlated with the star-forming activity over a whole cloud. Furthermore, we found that the log-normal N-PDF parameters obtained from the fitting showed two impressive features. First, the log-normal distribution at the low-density part had the same mean column density (\sim 1021.5^{21.5} cm2^{-2}) for almost all the molecular clouds. Second, the width of the log-normal distribution tended to decrease with an increasing mean density of the structures. These correlations suggest that the shape of the N-PDF reflects the relationship between the density and turbulent structure of the whole molecular cloud but is less affected by star-forming activities.

Keywords

Cite

@article{arxiv.2305.07094,
  title  = {Multi log-normal density structure in Cygnus-X molecular clouds: A fitting for N-PDF without power-law},
  author = {Takeru Murase and Toshihiro Handa and Ren Matsusaka and Yoshito Shimajiri and Masato I. N. Kobayashi and Mikito Kohno and Junya Nishi and Norimi Takeba and Yosuke Shibata},
  journal= {arXiv preprint arXiv:2305.07094},
  year   = {2023}
}

Comments

14 pages, 7 Figures, Accepted in MNRAS

R2 v1 2026-06-28T10:32:26.418Z