标题： dm2gal：用神经网络将暗物质映射到星系 显示英文标题

标题： dm2gal: Mapping Dark Matter to Galaxies with Neural Networks

摘要： 由星系巡天产生的宇宙结构图是回答关于宇宙基本问题的关键工具。 这些量的准确理论预测对于最大化这些项目的科学回报是必要的。 通过包括引力和流体力学来模拟宇宙是完成这一目标最强大的技术之一；不幸的是，这些模拟在计算上非常昂贵。 另一种方法是仅考虑引力的模拟，这更便宜，但无法预测宇宙网中星系的位置和特性。 在这项工作中，我们使用卷积神经网络在仅考虑引力模拟生成的暗物质场之上绘制星系恒星质量。 星系的恒星质量对于巡天中的星系选择非常重要，因此是一个需要预测的重要量。 我们的模型优于最先进的基准模型，并允许生成快速且准确的观测星系分布模型。 显示英文摘要

摘要： 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.