AI 中文总结
本文首次将神经网络增强重建方法NERV应用于真实星系巡天数据，通过卷积神经网络恢复被非线性增长衰减的BAO信号，在BOSS DR12样本上显著提升距离测量精度，为DESI等巡天提供实用工具。
AI 中文摘要
我们首次将基于神经网络的重子声学振荡（BAO）重建方法应用于真实星系巡天数据，恢复了因非线性结构增长而衰减的声学特征。NERV（神经网络增强的宇宙重建）通过使用在立方体N体模拟上训练的卷积神经网络来增强标准重建方法，并明确考虑了现实观测效应，包括弯曲天空几何、红移相关的选择函数以及有限的巡天边界，方法是将巡天体积划分为局部块。我们在MultiDark-Patchy模拟星表上验证了该方法，恢复了无偏的BAO膨胀参数。将NERV应用于BOSS DR12星系样本，显著提高了BAO距离测量的精度。这些结果确立了神经网络重建作为正在进行中的巡天项目（如DESI）中BAO分析的实际组成部分的地位，并有可能大幅收紧对宇宙膨胀历史和暗能量本质的约束。
英文摘要
We present the first application of neural-network-based baryon acoustic oscillation (BAO) reconstruction to real galaxy survey data, restoring the acoustic signature damped by nonlinear structure growth. {\texttt{NERV}} ({\bf N}eural-network {\bf E}nhanced {\bf R}econstruction of the Uni{\bf V}erse) augments standard reconstruction with a convolutional neural network trained on cubic $N$-body simulations, and explicitly accounts for realistic observational effects including the curved-sky geometry, the redshift-dependent selection function, and finite survey boundaries, by tessellating the survey volume into local patches. We validate the method on the \textsc{MultiDark-Patchy} mock catalogs, recovering unbiased BAO dilation parameters. Applied to the BOSS DR12 galaxy sample, NERV improves the precision of the BAO distance measurements significantly. These results establish neural reconstruction as a practical component of BAO analyses for ongoing surveys such as DESI, with the potential to substantially tighten constraints on the cosmic expansion history and the nature of dark energy.
