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arXiv 2407.03221astro-ph.CO
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使用神经网络模拟器建模 BOSS 空洞-星系互相关函数

Modelling the BOSS void-galaxy cross-correlation function using a neural-network emulator

Tristan S. Fraser, Enrique Paillas, Will J. Percival, Seshadri Nadathur, Slađana Radinović, Hans A. Winther

AI总结:

本文提出基于 AbacusSummit 训练神经网络模拟器的空洞-星系互相关建模方法,并用 BOSS 数据约束 ΛCDM,显著提升 Ω_m 等参数的测量精度。

摘要:

我们提出一种基于模拟器的方法,用于建模宇宙学空洞与星系之间的互相关。这使我们能够建模宇宙学对空洞寻找以及空洞-星系互相关函数形状的影响,从而改进以往基于模板的方法。我们利用 AbacusSummit 模拟套件训练神经网络,并拟合来自 Sloan Digital Sky Survey(斯隆数字巡天)Baryon Oscillation Spectroscopic Survey(重子振荡光谱巡天,BOSS)样本的数据。我们通过红移空间畸变(RSD)恢复关于结构增长的信息,并通过 Alcock-Paczyński(AP)效应恢复宇宙几何信息;在 ΛCDM 宇宙学中测得 Ω_m = 0.330±0.020,σ_8 = 0.777^{+0.047}_{-0.062}。与基于模板方法的结果相比,我们发现拟合空洞-星系互相关函数的形状可提供更多信息,并提升约束能力。相比之下,我们发现,如果假设空洞中心对 AP 效应具有与星系相同的响应——这是一种常见的简化——那么以往对 AP 测量误差的估计偏低。总体而言,采用这种新的、更全面的方法,我们使 Ω_m 的误差降低了 28%,而 σ_8 的误差与之相近。鉴于包括 DESI 和 Euclid 在内的未来巡天所具备的统计能力,我们预期所提出的方法将成为分析这些数据中空洞的新基线。

英文摘要:

We introduce an emulator-based method to model the cross-correlation between cosmological voids and galaxies. This allows us to model the effect of cosmology on void finding and on the shape of the void-galaxy cross-correlation function, improving on previous template-based methods. We train a neural network using the AbacusSummit simulation suite and fit to data from the Sloan Digital Sky Survey Baryon Oscillation Spectroscopic Survey sample. We recover information on the growth of structure through redshift-space distortions (RSD), and the geometry of the Universe through the Alcock-Paczyński (AP) effect, measuring $Ω_{\rm m} = 0.330\pm 0.020$ and $σ_8 = 0.777^{+0.047}_{-0.062}$ for a $Λ\rm{CDM}$ cosmology. Comparing to results from a template-based method, we find that fitting the shape of the void-galaxy cross-correlation function provides more information and leads to an improvement in constraining power. In contrast, we find that errors on the AP measurements were previously underestimated if void centres were assumed to have the same response to the AP effect as galaxies - a common simplification. Overall, we recover a $28\%$ reduction in errors for $Ω_{\rm{m}}$ and similar errors on $σ_8$ with our new, more comprehensive, method. Given the statistical power of future surveys including DESI and Euclid, we expect the method presented to become the new baseline for the analysis of voids in these data.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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