联合设定各向异性随机引力波背景多分量的上限
Jointly setting upper limits on multiple components of an anisotropic stochastic gravitational-wave background
AI总结：
本文研究如何利用PyStoch流程和Fisher信息矩阵联合分离并估计具有不同谱形状的各向异性随机引力波背景分量，并用Advanced LIGO第三次观测运行前半段数据设定了更稳健的多分量联合上限。
摘要：
随着引力波探测器灵敏度的不断提高以及更多探测器加入国际网络，探测到随机引力波背景（SGWB）的可能性正在逐步增大。不同的天体物理和宇宙学过程很可能产生具有不同谱特征和天空分布的背景。因此，观测到的SGWB将是这些分量的叠加。由此，在首次探测到SGWB之后，最先面临的问题之一很可能就是识别主导分量及其在天空中的分布。这两个问题在文献中曾被分别讨论，即如何分离各向同性背景的分量，以及如何探测单个分量的各向异性。本文研究的是如何分离具有（足够）不同谱形状的、不同的各向异性背景。我们首先利用高效分析流程PyStoch从折叠数据中获得组合Fisher信息矩阵，该流程纳入了像素与谱指数之间的协方差。这对于估计检测统计量并设定上限是必要的。不过，根据最近的一项研究，我们忽略了像素间噪声协方差，因为在当前探测器灵敏度水平下，它对结果没有显著影响。我们表明，联合分析能够准确分离并估计具有不同谱形状和不同天空分布的背景，且没有明显偏差。这确实以方差增大为代价。因此，联合上限虽然不如单独分析严格，但更加稳健。最后，我们利用Advanced LIGO在第三次观测运行前半段之前采集的数据，对多分量各向异性背景设定了联合上限。
英文摘要：
With the increasing sensitivities of the gravitational wave (GW) detectors and more detectors joining the international network, the chances of detection of a stochastic GW background (SGWB) are progressively increasing. Different astrophysical and cosmological processes are likely to give rise to backgrounds with distinct spectral signatures and distributions on the sky. The observed SGWB will therefore be a superposition of these components. Hence, one of the first questions that will come up after the first detection of a SGWB will likely be about identifying the dominant components and their distributions on the sky. Both these questions were addressed separately in the literature, namely, how to separate components of isotropic backgrounds and how to probe the anisotropy of a single component. Here, we address the question of how to separate distinct anisotropic backgrounds with (sufficiently) different spectral shapes. We first obtain the combined Fisher information matrix from folded data using an efficient analysis pipeline PyStoch, which incorporates covariances between pixels and spectral indices. This is necessary for estimating the detection statistic and setting upper limits. However, based on a recent study, we ignore the pixel-to-pixel noise covariance that does not have a significant effect on the results at the present sensitivity levels of the detectors. We show that the joint analysis accurately separates and estimates backgrounds with different spectral shapes and different sky distributions with no major bias. This does come at the cost of increased variance. Thus making the joint upper limits safer, though less strict than the individual analysis. We finally set joint upper limits on the multicomponent anisotropic background using Advanced LIGO data taken up to the first half of the third observing run.
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