论文标题
DM2GAL：用神经网络将暗物质映射到星系
dm2gal: Mapping Dark Matter to Galaxies with Neural Networks
论文作者
论文摘要
银河调查产生的宇宙结构地图是回答有关宇宙基本问题的关键工具之一。需要对这些数量的准确预测来最大程度地提高这些程序的科学回报。通过包括重力和流体动力学来模拟宇宙是实现这一目标的最强大技术之一。不幸的是，这些模拟在计算上非常昂贵。另外，仅重力模拟便宜，但不能预测星系在宇宙网络中的位置和特性。在这项工作中，我们使用卷积神经网络在仅重力模拟产生的暗物质场之上涂上星系恒星块。星系的恒星质量对于调查中的星系选择很重要，因此需要预测的重要数量。我们的模型的表现优于最新基准模型，并允许生成观察到的星系分布的快速准确模型。
Maps of cosmic structure produced by galaxy surveys are one of the key tools for answering fundamental questions about the Universe. Accurate theoretical predictions for these quantities are needed to maximize the scientific return of these programs. Simulating the Universe by including gravity and hydrodynamics is one of the most powerful techniques to accomplish this; unfortunately, these simulations are very expensive computationally. Alternatively, gravity-only simulations are cheaper, but do not predict the locations and properties of galaxies in the cosmic web. In this work, we use convolutional neural networks to paint galaxy stellar masses on top of the dark matter field generated by gravity-only simulations. Stellar mass of galaxies are important for galaxy selection in surveys and thus an important quantity that needs to be predicted. Our model outperforms the state-of-the-art benchmark model and allows the generation of fast and accurate models of the observed galaxy distribution.
