论文标题
学习宇宙学和宇宙图的聚类
Learning cosmology and clustering with cosmic graphs
论文作者
论文摘要
我们从骆驼项目的最先进的流体动力模拟项目中训练了数千个星系目录的深度学习模型，以执行回归和推理。我们采用图形神经网络（GNN），即旨在处理不规则和稀疏数据的架构，例如宇宙中星系的分布。我们首先表明，GNN可以学会以数百分比的精度计算星系目录的功率谱。然后，我们训练GNNS在星系场级别执行无可能的推断。我们的模型能够使用$ \ sim12 \％-13 \％$的$ω_ {\ rm m} $的价值，仅从$ \ sim1000 $ of $ \ sim1000 $ galaxies的$（25〜h^{ - 1} {\ rm mpc}）^$ z = 0 $的位置上的$（25〜h^{ - 1} {\ rm mpc}）的位置，以示为$ z = 0 $ y的模型。结合来自星系属性的信息，例如恒星质量，恒星金属性和恒星半径，将准确性提高到$ 4 \％-8 \％$。我们的模型是构建的，是转化和旋转不变的，它们可以从大于两个星系之间的最小距离的任何刻度中提取信息。但是，我们的模型并不是完全可靠的：对具有与训练的模拟进行的模拟测试不会作为准确的结果。
We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to work with irregular and sparse data, like the distribution of galaxies in the Universe. We first show that GNNs can learn to compute the power spectrum of galaxy catalogues with a few percent accuracy. We then train GNNs to perform likelihood-free inference at the galaxy-field level. Our models are able to infer the value of $Ω_{\rm m}$ with a $\sim12\%-13\%$ accuracy just from the positions of $\sim1000$ galaxies in a volume of $(25~h^{-1}{\rm Mpc})^3$ at $z=0$ while accounting for astrophysical uncertainties as modelled in CAMELS. Incorporating information from galaxy properties, such as stellar mass, stellar metallicity, and stellar radius, increases the accuracy to $4\%-8\%$. Our models are built to be translational and rotational invariant, and they can extract information from any scale larger than the minimum distance between two galaxies. However, our models are not completely robust: testing on simulations run with a different subgrid physics than the ones used for training does not yield as accurate results.
