Distance determination of molecular clouds in the first quadrant of the Galactic plane using deep learning: I. Method and results
Distance determination of molecular clouds in the first quadrant of the Galactic plane using deep learning: I. Method and results
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利用深度学习确定银河面第一象限分子云距离：一、方法与结果
DOI：
10.1093/pasj/psac104
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发表时间：
2023
影响因子：
2.3
通讯作者：
Yone
中科院分区：
文献类型：
--
作者：
Fujita Shinji;Ito Atsushi M.;Miyamoto Yusuke;Kawanishi Yasutomo;Torii Kazufumi;Shimajiri Yoshito;Nishimura Atsushi;Tokuda Kazuki;Ohnishi Toshikazu;Kaneko Hiroyuki;Inoue Tsuyoshi;Takekawa Shunya;Kohno Mikito;Ueda Shota;Nishimoto Shimpei;Yone
机器学习已经成功地应用于各个领域，但它是否是确定银河系分子云距离的可行工具是一个悬而未决的问题。在银河系中，运动学距离通常用来表示分子云的距离。然而，对于内星系，两种不同的解决方案，即，“近”解和“远”解可以同时导出。我们尝试使用卷积神经网络（CNN）构建两类（“近”或“远”）推理模型，这是一种可以捕获空间特征的深度学习形式。在这项研究中，我们使用了Nobeyama 45米射电望远镜获得的银河平面第一象限的CO数据集（l= 62°-10°，|B| < 1°）。在模型中，我们采用12 CO（J= 1-0）排放的三维分布（位置-位置-速度）作为主要输入。为了训练模型，从红外天文卫星WISE的Hiiregion目录中创建了具有“近”或“远”注释的数据集。因此，我们在训练数据集上构建了一个具有准确率的CNN模型。使用所提出的模型，我们确定的分子云的距离确定的CLUMPFIND算法。我们发现，在12 CO数据中发现的距离小于8.15 kpc的分子云的质量在质量范围M> 103 M内遵循幂律分布，指数约为-2.3。此外，还确定了从银河系北极观测到的银河系分子气体的详细分布。
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 the12CO (J= 1–0) emissions as the main input. To train the model, a dataset with “Near” or “Far” annotation was created from the Hiiregion catalog of the infrared astronomy satellite WISE. Consequently, we construct a CNN model with aaccuracy 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 the12CO data follows a power-law distribution with an index of approximately −2.3 in the mass rangeM> 103M⊙. In addition, the detailed molecular gas distribution of the Galaxy, as seen from the Galactic North pole, was determined.