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arXiv 2402.03518astro-ph.GAastro-ph.HE
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从模拟湍流气体的密度图中深度学习宇宙射线输运

Deep Learning Cosmic Ray Transport from Density Maps of Simulated, Turbulent Gas

Chad Bustard, John Wu

备注：
Accepted to Machine Learning: Science and Technology. 25 pages, 11 figures
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AI总结：

本研究利用卷积神经网络仅基于气体密度图分类宇宙射线输运机制，区分流动与扩散输运及扩散率大小，准确率达92%-99%，揭示输运对湍流相位信息的影响。

摘要：

银河宇宙射线（CRs）的粗粒度传播传统上受限于银河系CR传播的唯象模型，这些模型拟合各种直接和间接观测数据；然而，约束CR沿单个磁场线的细粒度输运——例如，扩散输运与流动输运模型——是一个未解决的挑战。利用最近的一组磁流体动力学湍流盒模拟训练集，其中CRs跨越一系列输运参数，我们使用仅基于气体密度图训练的卷积神经网络（CNNs）来分类CR输运机制。我们发现，即使是相对简单的CNNs也能相当有效地将密度切片分类到相应的CR输运参数，区分流动输运和扩散输运，以及扩散率的大小，类别准确率在$92\%$到$99\%$之间。如我们所示，CRs在气体上留下的依赖于输运的印记并非全部与最终的密度功率谱相关：即使图像谱被展平，分类准确率仍然很高（$85\%$到$98\%$的准确率），突出了CR输运依赖的湍流相位信息变化。我们用显著性图和图像修改来解释我们的结果，并讨论物理见解和未来应用。

英文摘要：

The coarse-grained propagation of Galactic cosmic rays (CRs) is traditionally constrained by phenomenological models of Milky Way CR propagation fit to a variety of direct and indirect observables; however, constraining the fine-grained transport of CRs along individual magnetic field lines -- for instance, diffusive vs streaming transport models -- is an unsolved challenge. Leveraging a recent training set of magnetohydrodynamic turbulent box simulations, with CRs spanning a range of transport parameters, we use convolutional neural networks (CNNs) trained solely on gas density maps to classify CR transport regimes. We find that even relatively simple CNNs can quite effectively classify density slices to corresponding CR transport parameters, distinguishing between streaming and diffusive transport, as well as magnitude of diffusivity, with class accuracies between $92\%$ and $99\%$. As we show, the transport-dependent imprints that CRs leave on the gas are not all tied to the resulting density power spectra: classification accuracies are still high even when image spectra are flattened ($85\%$ to $98\%$ accuracy), highlighting CR transport-dependent changes to turbulent phase information. We interpret our results with saliency maps and image modifications, and we discuss physical insights and future applications.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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