arXiv 2405.045472024-05-05gr-qc

基于LIGO数据的典型遗迹引力波约束

Constraints on typical relic gravitational waves based on data of LIGO

Minghui Zhang, Hao Wen

发表信息：: Communications in Theoretical Physics, Vol. 77, No. 8 (2025) 085401
DOI：: 10.1088/1572-9494/adb5f5

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AI总结：

本文利用卷积神经网络在LIGO O2、O3a和O3b数据中搜寻暴胀及一阶相变产生的遗迹引力波，未发现信号并给出谱能量密度上限10^{-5}，所提方法可推广至O4等未来数据。

摘要：

来自早期宇宙的遗迹引力波（RGWs）携带着基础性信息，因此从LIGO-Virgo网络等观测站的数据中搜寻RGW信号极其重要。本文聚焦于来自暴胀和一阶相变（通过声波和气泡碰撞产生）的典型RGWs，建立了有效且有针对性的深度学习神经网络，在真实LIGO数据（O2、O3a和O3b）中搜寻RGW信号。我们构建了卷积神经网络（CNN）来估计LIGO数据中存在目标RGW信号的可能性（通过定量数值和分布），或对其强度提供约束。我们发现，如果所构建的CNN能够恰当地估计RGW的参数，它可以准确（约94%至99%）判断样本是否包含RGW信号；如果不能恰当地估计参数，则CNN给出的可能性不可靠。在对大量LIGO数据集进行测试后，结果表明没有证据显示所关注理论预测的来自暴胀、声波或气泡碰撞的RGW存在，并给出了其引力波谱能量密度h^2Ω_{gw}的上限为10^{-5}（针对通过反向映射给出的特定参数区域内各种量级的引力波振幅）。简言之，获得了零结果和上限；我们为从LIGO数据中搜寻RGW而开发的方法和神经网络可能是有效且可靠的，不仅可应用于当前数据，也可应用于即将到来的O4数据或其他观测数据集，从而为探索潜在的RGW信号或为相关理论模型提供约束建立一个可用方案。

英文摘要：

Relic gravitational waves (RGWs) from early universe carry fundamental information, so it's extraordinarily important to search RGW signals from data of observatories like LIGO-Virgo network. Here, focusing on typical RGWs from inflation and first-order phase transition (by sound waves and bubble collisions), effective and targeted deep learning neural networks are established to search RGW signals among real LIGO data (O2, O3a and O3b). We construct Convolutional Neural Network (CNN) to estimate likelihood (by quantitative values and distributions) of existence of focused RGW signals in LIGO data, or provide constraints on their strengths. We find if the built CNN properly estimates the parameters of RGWs, it can accurately (about 94% to 99%) determine whether the samples contain RGW signals, and if not, the likelihood given by CNN is not reliable. After testing large amount of LIGO datasets, the results indicate no evidence of RGWs from inflation, sound waves, or bubble collisions predicted by focused theories, and it provides upper limits of their GW spectral energy densities of h^2Ω_{gw} of 10^{-5} (for various orders of GW amplitude given specific parameter regions by reverse mapping). In short, null results and upper limits are acquire; the methods and neural networks we develop to search RGWs from LIGO data could be effective and reliable, which can be applied not only for current data but also upcoming O4 data or other observational datasets, to establish an available scheme for exploring potential RGW signals or to provide constraints on relevant theoretical models.

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