在实地一级对宇宙推论的共温效应的明显边缘化 (Robust marginalization of baryonic effects for cosmological inference at the field level)

Francisco Villaescusa-Navarro,Shy Genel,Daniel Angles-Alcazar,David N. Spergel,Yin Li,Benjamin Wandelt,Leander Thiele,Andrina Nicola,Jose Manuel Zorrilla Matilla,Helen Shao,Sultan Hassan,Desika Narayanan,Romeel Dave,Mark Vogelsberger

from arxiv, 7 pages, 4 figures. Second paper of a series of four. The 2D maps, codes, and network weights used in this paper are publicly available at https://camels-multifield-dataset.readthedocs.io

We train neural networks to perform likelihood-free inference from $(25\,h^{-1}{\rm Mpc})^2$ 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project. We show that the networks can extract information beyond one-point functions and power spectra from all resolved scales ($\gtrsim 100\,h^{-1}{\rm kpc}$) while performing a robust marginalization over baryonic physics at the field level: the model can infer the value of $\Omega_{\rm m} (\pm 4\%)$ and $\sigma_8 (\pm 2.5\%)$ from simulations completely different to the ones used to train it.

翻译：我们训练神经网络,以便用$(25\,h\\\\\\\\\\\\\\\\\\\\\\\\\\\rm Mpc})2D的地图进行无概率的推断,这些地图含有CAMELS项目数千个流体动力模拟中的总质量表面密度。我们显示,这些网络能够从所有溶解的尺度($\gtrsim 100\,h\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\在\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\在\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\