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arXiv 2302.14591astro-ph.CO
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从晕和星系星表预测$Ω_{\rm m}$的普适方程

A universal equation to predict $Ω_{\rm m}$ from halo and galaxy catalogues

Helen Shao, Natalí S. M de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Angles-Alcazar, Shy Genel, Lars Hernquist, Ulrich P. Steinw… 展开作者

Helen Shao, Natalí S. M de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Angles-Alcazar, Shy Genel, Lars Hernquist, Ulrich P. Steinwandel, Tiago Castro, Elena Hernandez-Martınez, Klaus Dolag, Christopher C. Lovell, Eli Visbal, Lehman H. Garrison, Mihir Kulkarni

摘要:

我们发现的解析方程,可以推断的$Ω_{\rm m}$的值从晕和星系目录的位置和速度模量。该方程是通过将定制的图神经网络(GNN)架构与符号回归相结合来推导的。我们首先训练GNN暗物质晕从Gadget N体模拟执行场级似然自由推理,并表明,我们的模型可以推断$Ω_{\rm m}$与$\sim6\%$的准确性从晕目录的数千个N体模拟运行六个不同的代码:Abacus,CUBEP $^3$ M,Gadget,恩佐,PKDGrav 3,和拉美西斯。通过将符号回归应用于包括GNN的不同部分,我们推导出可以从使用所有上述代码运行的模拟的光环目录预测$Ω_{\rm m}$的方程,其精度与GNN的相似。我们表明,通过调整一个单一的自由参数,我们的方程也可以推断出$Ω_{\rm m}$的价值,从星系目录的数以千计的国家的最先进的流体动力学模拟的CAMELS项目,每个不同的天体物理模型,运行五个不同的代码,采用不同的次网格物理:IllustrisTNG,SIMBA,阿斯特丽德,Magneticum,SWIFT-EAGLE。此外,方程也表现良好时,从模拟涵盖了参数空间中的一个巨大的区域,在5个宇宙学和23个天体物理参数的样本变化的星系目录进行测试。我们推测,方程可能反映了存在的一个基本的物理关系之间的相空间分布的通用示踪剂和$Ω_{\rm m}$,一个是不受星系形成物理小到$10~h^{-1}{\rm kpc}$的尺度。

英文摘要:

We discover analytic equations that can infer the value of $Ω_{\rm m}$ from the positions and velocity moduli of halo and galaxy catalogues. The equations are derived by combining a tailored graph neural network (GNN) architecture with symbolic regression. We first train the GNN on dark matter halos from Gadget N-body simulations to perform field-level likelihood-free inference, and show that our model can infer $Ω_{\rm m}$ with $\sim6\%$ accuracy from halo catalogues of thousands of N-body simulations run with six different codes: Abacus, CUBEP$^3$M, Gadget, Enzo, PKDGrav3, and Ramses. By applying symbolic regression to the different parts comprising the GNN, we derive equations that can predict $Ω_{\rm m}$ from halo catalogues of simulations run with all of the above codes with accuracies similar to those of the GNN. We show that by tuning a single free parameter, our equations can also infer the value of $Ω_{\rm m}$ from galaxy catalogues of thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, each with a different astrophysics model, run with five distinct codes that employ different subgrid physics: IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE. Furthermore, the equations also perform well when tested on galaxy catalogues from simulations covering a vast region in parameter space that samples variations in 5 cosmological and 23 astrophysical parameters. We speculate that the equations may reflect the existence of a fundamental physics relation between the phase-space distribution of generic tracers and $Ω_{\rm m}$, one that is not affected by galaxy formation physics down to scales as small as $10~h^{-1}{\rm kpc}$.

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