论文标题
卷积神经网络的应用预测湍流云中的磁场方向
Application of Convolutional Neural Networks to Predict Magnetic Fields Directions in Turbulent Clouds
论文作者
论文摘要
我们采用深度学习方法CASI-3D（结构识别-3D的卷积方法）来推断从分子线发射中亚/跨/跨性别湍流云中磁场的方向。我们进行具有不同磁场强度的磁性水力学模拟，并使用它们来产生合成观测。我们将3D辐射传输代码RADMC-3D应用于模拟云中的12CO和13CO（J = 1-0）线发射，然后在这些线排放数据方面训练CASI-3D模型，以预测像素级别的磁场形态。训练有素的CASI-3D模型能够以低误差（<10deg for Alfenty样品为<10DEG）推断磁场方向，用于反式阿尔植物样品的<30DEG）。我们此外，我们在金牛座的真实亚/跨环境区域测试CASI-3D的性能。 CASI-3D预测与普朗克尘埃极化测量所推论的磁场方向一致。我们使用开发的方法生成金牛座的新磁场图，该图的角分辨率比Planck地图高三倍。
We adopt the deep learning method CASI-3D (Convolutional Approach to Structure Identification-3D) to infer the orientation of magnetic fields in sub-/trans- Alfvenic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and use these to generate synthetic observations. We apply the 3D radiation transfer code RADMC-3d to model 12CO and 13CO (J = 1-0) line emission from the simulated clouds and then train a CASI-3D model on these line emission data cubes to predict magnetic field morphology at the pixel level. The trained CASI-3D model is able to infer magnetic field directions with low error (< 10deg for sub-Alfvenic samples and <30deg for trans-Alfvenic samples). We furthermore test the performance of CASI-3D on a real sub-/trans- Alfvenic region in Taurus. The CASI-3D prediction is consistent with the magnetic field direction inferred from Planck dust polarization measurements. We use our developed methods to produce a new magnetic field map of Taurus that has a three-times higher angular resolution than the Planck map.
