Distance determination of molecular clouds in the first quadrant of the Galactic plane using deep learning: I. Method and results

Abstract

Abstract Machine learning has been successfully applied in various 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 to represent the distance to a molecular cloud. However, for the inner Galaxy, two different solutions, i.e., the “Near” solution and the “Far” solution, can be derived simultaneously. We attempt to construct a two-class (“Near” or “Far”) inference model using a convolutional neural network (CNN), which is a form of deep learning that can capture spatial features generally. In this study, we use the CO dataset in the first quadrant of the Galactic plane obtained with the Nobeyama 45 m radio telescope (l = 62°–10°, |b| < 1°). In the model, we apply the three-dimensional distribution (position–position–velocity) of the 12CO (J = 1–0) emissions as the main input. To train the model, a dataset with “Near” or “Far” annotation was created from the H ii region catalog of the infrared astronomy satellite WISE. Consequently, we construct a CNN model with a $76\% $ accuracy rate on the training dataset. Using the proposed model, we determine the distance to the molecular clouds identified by the CLUMPFIND algorithm. We found that the mass of molecular clouds with a distance of <8.15 kpc identified in the 12CO data follows a power-law distribution with an index of approximately −2.3 in the mass range M > 103 M⊙. In addition, the detailed molecular gas distribution of the Galaxy, as seen from the Galactic North pole, was determined.