Distances and Statistics of Local Molecular Clouds in the First Galactic Quadrant

Abstract

We present an analysis of local molecular clouds ( km s−1, i.e., <1.5 kpc) in the first Galactic quadrant ( and ), a pilot region of the Milky Way Imaging Scroll Painting (MWISP) CO survey. Using the Spectral Clustering for Interstellar Molecular Emission Segmentation algorithm to divide large molecular clouds into moderate-size ones, we determined distances to 28 molecular clouds with the background-eliminated extinction-parallax method using the Gaia DR2 parallax measurements aided by A G and A V ; the distance ranges from 250 to about 1.5 kpc. These incomplete distance samples indicate a linear relationship between the distance and the radial velocity (V LSR) with a scatter of 0.16 kpc, and kinematic distances may be systematically larger for local molecular clouds. In order to investigate fundamental properties of molecular clouds, such as the total sample number, the line width, the brightness temperature, the physical area, and the mass, we decompose the spectral cube using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Post-selection criteria are imposed on DBSCAN clusters to remove the noise contamination, and we found that the separation of molecular cloud individuals is reliable based on a definition of independent consecutive structures in l–b–V space. The completeness of the local molecular cloud flux collected by the MWISP CO survey is about 80%. The physical area, A, shows a power-law distribution, dN/d , while the molecular cloud mass also follows a power-law distribution but is slightly flatter, dN/dM ∝ M −1.96 ± 0.11.

The Spiral Structure in the Solar Neighborhood

“…In the Milky Way, GMCs have long been known as good tracers of spiral arms (e.g., Myers et al, 1986;Grabelsky et al, 1988;Hou and Han, 2014). From the wealthy dataset of Galactic CO surveys (see Heyer and Dame, 2015, for a review), a large number of isolated molecular clouds have been identified by different methods (e.g., García et al, 2014;Rice et al, 2016;Miville-Deschênes et al, 2017;Yan et al, 2020;Duarte-Cabral et al, 2021). For the majority of them, only kinematic distances are known, which depend on the adopted Galaxy rotation curve, the solution of the kinematic distance ambiguity, and deviation from the hypothetic circular rotation.…”

“…The distance uncertainties of 11 clouds were ≲ 10%. With the same method, Yan et al (2020) determined the distances for 28 local molecular clouds (d < 1.5 kpc, here d is the distance to the Sun) in the first Galactic quadrant. The distances for 76 molecular clouds were measured in the second Galactic quadrant by Yan et al (2021).…”

Distances to molecular clouds in the second Galactic quadrant

“…Before measuring distances, we drew molecular cloud samples using the method developed by Yan et al (2020), which is based on the DBSCAN clustering algorithm 4 . DBSCAN identifies connected structures in position-position-velocity (PPV) space, paying no attention to the internal structure, the morphology, or the scale of molecular clouds.…”

“…In PPV space, corresponds to three connectivity types ( = 1, √ 2, and √ 3), and, for high values of , MinPts also needs to be high to avoid including too many noise samples. Yan et al (2020) found that, for appropriate lower MinPts values, the three connectivity types yield similar cloud samples, so we used connectivity 1 ( = 1) and MinPts 4. The minimum cutoff on the PPV data cubes is 2σ (∼1 K).…”

“…• 25), which is mapped by the MWISP survey with high sensitivities and moderate angular resolutions (Du et al 2016(Du et al , 2017Sun et al 2020), including images of three CO isotopolog lines. For the purpose of distance determination, we only use the 12 CO (J = 1 → 0) images to identify molecular clouds with the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm (Yan et al 2020), whose distances are subsequently examined with the BEEP method (Yan et al 2019a).…”