论文标题
各向异性随机引力背景的统一地图
Unified mapmaking for an anisotropic stochastic gravitational wave background
论文作者
论文摘要
在未来几年中，可以检测到由未解决的天体物理来源的信号叠加产生的随机引力背景（SGWB）。因此，估计天空中强度的变化可以在提高我们对天体物理模型的理解中起关键作用。已经针对先进的地面干涉探测器的所有数据研究生产了Skymap。尽管这些地图是在像素和SPH碱基中生成的，但分别探测了局部和弥漫性的天体物理和宇宙学来源，其算法采用互相关为共同的策略，但基础代数和数值实施仍然不同。结果，在这些分析中需要在两个基础上产生Skymap。我们表明，这些明显的冗余方法确实可以统一到单个分析，该分析可以探测非常不同的尺度并通过将其应用于真实数据来证明它。我们首先开发代数以表明两个不同基础中的结果很容易转换。然后，我们将这两个方案都纳入了各向异性SGWB（Pystoch）的现在标准分析管道中。这将使SGWB各向异性搜索以SPH为基础，也可以充分利用集成的\ HPX工具，并使在每个频率箱中执行搜索在计算上可行。但是，我们遵循另一种方法来直接估计SPH矩。我们表明，从这些不同方法获得的结果非常匹配。 SPH时刻的差异小于$ 0.1 $ \％，对于Fisher Information Matrices，差异不到$ 0.01 $ \％。因此，我们得出的结论是，单个Skymap足以描述随机背景中的各向异性。 Pystoch的多重能力将有助于估计和约束表征各向异性背景的各种度量。
A stochastic gravitational wave background (SGWB), created by the superposition of signals from unresolved astrophysical sources, may be detected in the next few years. Estimating the variation of intensity across the sky can, therefore, play a key role in improving our understanding of astrophysical models. Skymaps have been produced for all the data-taking runs of the advanced ground-based interferometric detectors. While these maps are being produced in pixel and SpH bases, to probe, respectively, localized and diffuse astrophysical and cosmological sources, with algorithms that employ cross-correlation as the common strategy, the underlying algebra and numerical implementation remain different. As a consequence, there was a need for producing skymaps in both bases in those analyses. We show that these manifestly redundant methods could indeed be unified to a single analysis that can probe very different scales and demonstrate it by applying them on real data. We first develop the algebra to show that the results in two different bases are easily transformable. We then incorporate both the schemes in the now-standard analysis pipeline for anisotropic SGWB, PyStoch. This will enable SGWB anisotropy searches in SpH basis also to take full advantage of integrated \hpx tools and makes it computationally feasible to perform the search in every frequency bin. We, however, follow a different approach for direct estimation of the SpH moments. We show that the results obtained from these different methods match very well; the differences are less than $0.1$\% for the SpH moments and less than $0.01$\% for the Fisher information matrices. Thus we conclude that a single skymap will be sufficient to describe the anisotropies in a stochastic background. The multiple capabilities of PyStoch will be useful for estimating and constraining various measures that characterize an anisotropic background.
