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arXiv 2001.04506astro-ph.GAastro-ph.IM
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应用卷积神经网络识别CO辐射中的恒星反馈泡

Application of Convolutional Neural Networks to Identify Stellar Feedback Bubbles in CO Emission

Duo Xu, Stella S. R. Offner, Robert Gutermuth, Colin Van Oort

摘要:

我们采用了深度学习方法CASI(卷积方法识别壳层),并将其扩展到3D(CASI-3D),以识别分子线光谱中恒星反馈的特征,如13 CO。我们采用磁流体动力学模拟,研究恒星风在湍流分子云中的影响作为输入,以产生合成观测。我们应用三维辐射传输程序radmc-3d模拟了模拟云的13 CO(J=1-0)谱线发射。我们训练两个CASI-3d模型:ME 1只预测反馈的位置,而MF预测每个体素中来自反馈的质量分数。我们采用75%的合成观测值作为训练集,并使用剩余数据评估两个模型的准确性。我们证明,模型ME 1识别模拟数据中的气泡具有95%的准确度,模型MF预测的气泡质量在4%的真实值。我们使用之前在13 CO中的金牛座中视觉识别的气泡来验证模型,并显示两者在最高置信度的气泡上表现良好。我们应用我们的两个模型上的完整的98 deg 2 FCRAO 13 CO调查金牛座云。模型ME 1和MF预测的反馈气体质量分别为2894 M和302 M。当包括由于13 CO数据立方体的有限速度范围而丢失的能量的校正因子时,模型ME 1预测在减去/不减去云速度梯度的情况下,反馈动能为4.0* 1 e46尔格和1.5* 1 e47尔格。MF模式预测的反馈动能为9.6* 1 e45 ergs和2.8* 1 e46 ergs,有/没有减去云速度梯度。模型ME 1预测的气泡位置和特性与之前目视识别的气泡一致。然而,模型MF表明,反馈特性计算的基础上视觉识别显着高估,由于视线混乱和污染的背景和前景气体。

英文摘要:

We adopt the deep learning method CASI (Convolutional Approach to Shell Identification) and extend it to 3D (CASI-3D) to identify signatures of stellar feedback in molecular line spectra, such as 13CO. We adopt magneto-hydrodynamics simulations that study the impact of stellar winds in a turbulent molecular cloud as an input to generate synthetic observations. We apply the 3D radiation transfer code radmc-3d to model 13CO (J=1-0) line emission from the simulated clouds. We train two CASI-3d models: ME1 predicts only the position of feedback, while MF predicts the fraction of the mass coming from feedback in each voxel. We adopt 75% of the synthetic observations as the training set and assess the accuracy of the two models with the remaining data. We demonstrate that model ME1 identifies bubbles in simulated data with 95% accuracy, and model MF predicts the bubble mass within 4% of the true value. We use bubbles previously visually identified in Taurus in 13CO to validate the models and show both perform well on the highest confidence bubbles. We apply our two models on the full 98 deg2 FCRAO 13CO survey of the Taurus cloud. Models ME1 and MF predict feedback gas mass of 2894 M and 302 M, respectively. When including a correction factor for missing energy due to the limited velocity range of the 13CO data cube, model ME1 predicts feedback kinetic energies of 4.0*1e46 ergs and 1.5*1e47 ergs with/without subtracting the cloud velocity gradient. Model MF predicts feedback kinetic energy of 9.6*1e45 ergs and 2.8*1e46 ergs with/without subtracting the cloud velocity gradient. Model ME1 predicts bubble locations and properties consistent with previous visually identified bubbles. However, model MF demonstrates that feedback properties computed based on visual identifications are significantly overestimated due to line of sight confusion and contamination from background and foreground gas.

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