Distance determination of molecular clouds in the 1st quadrant of the Galactic plane using deep learning : I. Method and Results

Abstract

Machine learning has been successfully applied in varied field but whether it is a viable tool for determining the distance to molecular clouds in the Galaxy is an open question. In the Galaxy, the kinematic distance is commonly employed as the distance to a molecular cloud. However, there is a problem in that for the inner Galaxy, two different solutions, the ``Near'' solution, and the ``Far'' solution, can be derived simultaneously. We attempted to construct a two-class (``Near'' or ``Far'') inference model using a Convolutional Neural Network (CNN), a form of deep learning that can capture spatial features generally. In this study, we used the CO dataset toward the 1st quadrant of the Galactic plane obtained with the Nobeyama 45-m radio telescope (l = 62-10 degree, |b| < 1 degree). In the model, we applied the three-dimensional distribution (position-position-velocity) of the 12CO (J=1-0) emissions as the main input. The dataset with ``Near'' or ``Far'' annotation was made from the HII region catalog of the infrared astronomy satellite WISE to train the model. As a result, we could construct a CNN model with a 76% accuracy rate on the training dataset. By using the model, we determined the distance to molecular clouds identified by the CLUMPFIND algorithm. We found that the mass of the molecular clouds with a distance of < 8.15 kpc identified in the 12CO data follows a power-law distribution with an index of about -2.3 in the mass range of M >10^3 Msun. Also, the detailed molecular gas distribution of the Galaxy as seen from the Galactic North pole was determined.

29 pages, 12 figures

References in corpus (18)

• Deep Learning in Neural Networks: An Overview
• Trigonometric Parallaxes Of High-Mass Star Forming Regions: Our View Of The Milky Way
• The Boston University-Five College Radio Astronomy Observatory Galactic Ring Survey
• The Structure of the Milky Way's Bar Outside the Bulge
• FOREST Unbiased Galactic plane Imaging survey with the Nobeyama 45-m telescope (FUGIN) I: Project Overview and Initial Results
• Giant Molecular Cloud Catalogues for PHANGS-ALMA: Methods and Initial Results
• The Perils of Clumpfind: The Mass Spectrum of Sub-structures in Molecular Clouds
• Revealing the physical properties of molecular gas in Orion with a large scale survey in J=2-1 lines of 12CO, 13CO and C18O
• The SEDIGISM survey: first data release and overview of the Galactic structure
• High abundance ratio of ${}^{13}$CO to C${}^{18}$O toward photon-dominated regions in the Orion-A giant molecular cloud
• CO Multi-line Imaging of Nearby Galaxies (COMING) IV. Overview of the Project
• A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
• FOREST Unbiased Galactic plane Imaging survey with the Nobeyama 45-m telescope (FUGIN) : Molecular clouds toward W33 ; possible evidence for a cloud-cloud collision triggering O star formation
• CO Multi-line Imaging of Nearby Galaxies (COMING): I. Physical properties of molecular gas in the barred spiral galaxy NGC 2903
• Deep Learning Assessment of galaxy morphology in S-PLUS DataRelease 1
• Uncovering distinct environments in an extended physical system around the W33 complex
• Nobeyama 45 m Local Spur CO survey. I. Giant molecular filaments and cluster formation in the Vulpecula OB association
• Autonomous Gaussian decomposition of the Galactic Ring Survey. II. The Galactic distribution of 13CO

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