论文标题
来自空白特性的机器学习宇宙学
Machine-learning cosmology from void properties
论文作者
论文摘要
宇宙空隙是宇宙中最大，最不足的结构。他们的属性已被证明可以编码有关宇宙法律和成分的宝贵信息。我们表明，机器学习技术可以在宇宙参数推理中解锁空白特征中的信息。我们依靠Gigantes数据集中的数千个空隙目录，其中每个目录都包含$ 1〜（H^{ - 1} {\ rm GPC} {\ rm GPC}）^3 $的平均11,000个空隙。我们专注于宇宙空隙的三个特性：椭圆度，密度对比度和半径。我们训练1）完全连接的神经网络，从单个空隙性质的直方图和2）从空隙目录的深度集进行，以对宇宙学参数的值进行无可能的推断。我们发现，我们的最佳模型能够分别限制$ω_ {\ rm m} $，$σ_8$和$ n_s $的价值，而$ n_s $分别为$ 10 \％$，$ 4 \％$和$ 3 \％$的平均相对误差，而无需从void Catalogs中使用任何空间信息。我们的结果为使用机器学习用空隙来限制宇宙学的说明提供了一个例证。
Cosmic voids are the largest and most underdense structures in the Universe. Their properties have been shown to encode precious information about the laws and constituents of the Universe. We show that machine learning techniques can unlock the information in void features for cosmological parameter inference. We rely on thousands of void catalogs from the GIGANTES dataset, where every catalog contains an average of 11,000 voids from a volume of $1~(h^{-1}{\rm Gpc})^3$. We focus on three properties of cosmic voids: ellipticity, density contrast, and radius. We train 1) fully connected neural networks on histograms from individual void properties and 2) deep sets from void catalogs, to perform likelihood-free inference on the value of cosmological parameters. We find that our best models are able to constrain the value of $Ω_{\rm m}$, $σ_8$, and $n_s$ with mean relative errors of $10\%$, $4\%$, and $3\%$, respectively, without using any spatial information from the void catalogs. Our results provide an illustration for the use of machine learning to constrain cosmology with voids.
