标题： 深度学习在宇宙结构形成中的见解 显示英文标题

标题： Deep learning insights into cosmological structure formation

摘要： 线性初始条件在早期宇宙中演变为晚期的暗物质晕可以使用宇宙学模拟来计算。 然而，对这一复杂过程的理论理解仍然难以捉摸；特别是初始条件中的各向异性信息在确定最终暗物质晕质量中的作用仍然是一个长期存在的谜题。 在这里，我们建立了一个深度学习框架来研究这个问题。 我们训练了一个三维卷积神经网络（CNN），从初始条件预测暗物质晕的质量，并全面量化初始密度场各向同性和各向异性方面关于最终晕质量的信息量。 我们发现，各向异性在球面平均密度场所包含的信息基础上，增加了一小部分但统计上显著的信息。 然而，最终质量预测的整体散射不会因这种额外信息而定性改变，仅从0.9 dex减少到0.7 dex。 鉴于这种微小的改进，我们的结果表明，初始密度场的各向同性方面基本上已经饱和了关于最终晕质量的相关信息。 因此，与其直接寻找初始条件各向异性中编码的信息，更可行的方法是基于如扰动理论等近似动力学信息来提高晕质量预测的准确性与速度。 更广泛地说，我们的结果表明，深度学习框架可以成为提取宇宙结构形成物理洞察力的强大工具。 显示英文摘要

摘要： The evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial conditions in establishing the final mass of dark matter halos remains a long-standing puzzle. Here, we build a deep learning framework to investigate this question. We train a three-dimensional convolutional neural network (CNN) to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses. We find that anisotropies add a small, albeit statistically significant amount of information over that contained within spherical averages of the density field about final halo mass. However, the overall scatter in the final mass predictions does not change qualitatively with this additional information, only decreasing from 0.9 dex to 0.7 dex. Given such a small improvement, our results demonstrate that isotropic aspects of the initial density field essentially saturate the relevant information about final halo mass. Therefore, instead of searching for information directly encoded in initial conditions anisotropies, a more promising route to accurate, fast halo mass predictions is to add approximate dynamical information based e.g. on perturbation theory. More broadly, our results indicate that deep learning frameworks can provide a powerful tool for extracting physical insight into cosmological structure formation.