arXiv 2205.129642022-05-25astro-ph.CO

鲁棒神经网络增强的局部原始非高斯性估计

Robust Neural Network-Enhanced Estimation of Local Primordial Non-Gaussianity

Utkarsh Giri, Moritz Münchmeyer, Kendrick M. Smith

DOI：: 10.1103/PhysRevD.107.L061301

摘要：

当应用于宇宙的非线性物质分布时，神经网络已被证明是非常统计敏感的宇宙学参数探针，例如线性扰动振幅$σ_8$。然而，当被用作“黑盒子”时，神经网络对重子不确定性不具有鲁棒性。我们提出了一个强大的架构约束原始非高斯$f_{NL}$，通过训练神经网络本地估计$σ_8$，并将这些本地估计与大规模密度场。我们将我们的方法应用于N体模拟，并表明，$σ(f_{NL})$是3.5倍优于从标准的基于光环的方法获得的约束。我们表明，我们的方法具有相同的鲁棒性大规模晕偏差：重子物理可以改变估计的$f_{NL}$的归一化，但不能改变是否检测到$f_{NL}$。

英文摘要：

When applied to the non-linear matter distribution of the universe, neural networks have been shown to be very statistically sensitive probes of cosmological parameters, such as the linear perturbation amplitude $σ_8$. However, when used as a "black box", neural networks are not robust to baryonic uncertainty. We propose a robust architecture for constraining primordial non-Gaussianity $f_{NL}$, by training a neural network to locally estimate $σ_8$, and correlating these local estimates with the large-scale density field. We apply our method to N-body simulations, and show that $σ(f_{NL})$ is 3.5 times better than the constraint obtained from a standard halo-based approach. We show that our method has the same robustness property as large-scale halo bias: baryonic physics can change the normalization of the estimated $f_{NL}$, but cannot change whether $f_{NL}$ is detected.

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