论文标题
强大的现场级别与暗物质光环
Robust field-level inference with dark matter halos
论文作者
论文摘要
我们将图形的神经网络训练来自小工具N体模拟的光环目录，以执行宇宙学参数的无现场级别的推断。目录中包含$ \ lyssim $ 5,000 halos with Masses $ \ gtrsim 10^{10} 〜h^{ - 1} m_ \ odot $，定期卷为$（25〜H^{ - 1} {\ rm mpc} {\ rm mpc}）^3 $;目录中的每个光环都具有多种特性，例如位置，质量，速度，浓度和最大圆速度。我们的模型构建为置换，翻译和旋转的不变性，并不施加最低限度的规模来提取信息，并能够推断出$ω_ {\ rm m m} $和$σ_8$的值，而$ \ sim6 \％$的平均相对误差为$ \ sim6 \％$，当时使用位置和速率和质量均等级别的相对误差。更重要的是，我们发现我们的模型非常强大：当使用五个不同的nbody代码运行的halo目录进行测试时，他们可以推断出$ω_ {\ rm m} $和$σ_8$的值，并进行$σ_8$。令人惊讶的是，经过训练的$ω_ {\ rm m} $训练的模型在对数千个最先进的骆驼水力动力模拟进行测试时也可以使用，该模拟运行了四个不同的代码和子网格物理学实现。使用诸如浓度和最大循环速度之类的光环特性允许我们的模型提取更多信息，以破坏模型的鲁棒性。这可能会发生，因为不同的N体代码不会在与这些参数相对应的相关尺度上收敛。
We train graph neural networks on halo catalogues from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogues contain $\lesssim$5,000 halos with masses $\gtrsim 10^{10}~h^{-1}M_\odot$ in a periodic volume of $(25~h^{-1}{\rm Mpc})^3$; every halo in the catalogue is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of $Ω_{\rm m}$ and $σ_8$ with a mean relative error of $\sim6\%$, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of $Ω_{\rm m}$ and $σ_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer $Ω_{\rm m}$ also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N-body codes are not converged on the relevant scales corresponding to these parameters.
