Dependence of Molecular Cloud Samples on Angular Resolution, Sensitivity, and Algorithms

TL;DR

This study examines how observational parameters like resolution and sensitivity, along with clustering algorithms, influence the identification and characterization of molecular cloud samples in galactic surveys.

Contribution

It demonstrates that molecular cloud catalogs are highly dependent on observational settings and algorithm choices, affecting their boundary definitions and counts.

Findings

Cloud boundaries vary with resolution and sensitivity.

High resolution reveals GMCs as clusters of smaller clouds.

Catalogs are non-unique and sensitive to observational parameters.

Abstract

In this work, we investigate the observational and algorithmic effects on molecular cloud samples identified from position-position-velocity (PPV) space. By smoothing and cutting off the high quality data of the Milky Way Imaging Scroll Painting (MWISP) survey, we extract various molecular cloud samples from those altered data with the DBSCAN (density-based spatial clustering of applications with noise) algorithm. Those molecular cloud samples are subsequently used to gauge the significance of sensitivity, angular/velocity resolution, and DBSCAN parameters. Two additional surveys, the FCRAO Outer Galaxy Survey (OGS) and the CfA-Chile 1.2 m complete CO (CfA-Chile) survey, are used to verify the MWISP results. We found that molecular cloud catalogs are not unique and the boundary and therefore the number shows strong variation with angular resolution and sensitivity. At low angular resolution (large beam sizes), molecular clouds merge together in PPV space, while low sensitivity (high cutoffs) misses small faint molecular clouds and takes bright parts of large molecular clouds as single ones. At high angular resolution and sensitivity, giant molecular clouds (GMCs) are resolved into individual clouds, and their diffuse components are also revealed. Consequently, GMCs are more appropriately interpreted as clusters or aggregates of molecular clouds, i.e., GMCs represent molecular cloud samples themselves.