多领域宇宙学与人工智能
Multifield Cosmology with Artificial Intelligence
摘要：
天体物理过程，如超新星和活动星系核的反馈，以一种知之甚少的方式改变了暗物质、气体和星系的性质和空间分布。这种不确定性是从宇宙学调查中提取信息的主要理论障碍之一。我们使用CAMELS项目的2，000个最先进的流体动力学模拟，涵盖各种宇宙学和天体物理学模型，并为13个不同领域生成数十万张二维地图：从暗物质到气体和恒星属性。我们使用这些映射来训练卷积神经网络，以提取最大量的宇宙学信息，同时在场水平上边缘化天体物理效应。虽然我们的地图只覆盖了$(25~h^{-1}{\rm Mpc})^2$的一小部分区域，而且不同的区域以非常不同的方式受到天体物理效应的污染，但我们的网络可以推断出$Ω_{\rm m}$和$σ_8$的值，对于大多数区域来说，精度只有百分之几。我们发现，与在没有受到天体物理效应污染的仅重力N体模拟的地图上训练的模型相比，网络执行的边缘化保留了丰富的宇宙学信息。最后，我们训练我们的网络在多个领域-二维地图，其中包含几个领域作为不同的颜色或通道-并发现，他们不仅可以推断出所有参数的值比网络更高的精度在个别领域训练，但他们可以约束$Ω_{\rm m}$的值比从N体模拟地图更高的精度。
英文摘要：
Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical obstacles to extract information from cosmological surveys. We use 2,000 state-of-the-art hydrodynamic simulations from the CAMELS project spanning a wide variety of cosmological and astrophysical models and generate hundreds of thousands of 2-dimensional maps for 13 different fields: from dark matter to gas and stellar properties. We use these maps to train convolutional neural networks to extract the maximum amount of cosmological information while marginalizing over astrophysical effects at the field level. Although our maps only cover a small area of $(25~h^{-1}{\rm Mpc})^2$, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of $Ω_{\rm m}$ and $σ_8$ with a few percent level precision for most of the fields. We find that the marginalization performed by the network retains a wealth of cosmological information compared to a model trained on maps from gravity-only N-body simulations that are not contaminated by astrophysical effects. Finally, we train our networks on multifields -- 2D maps that contain several fields as different colors or channels -- and find that not only they can infer the value of all parameters with higher accuracy than networks trained on individual fields, but they can constrain the value of $Ω_{\rm m}$ with higher accuracy than the maps from the N-body simulations.
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。
