标题： 各向异性随机引力波背景的统一测绘 显示英文标题

标题： Unified mapmaking for an anisotropic stochastic gravitational wave background

摘要： 由未解析的天体物理源信号叠加产生的随机引力波背景（SGWB），可能在接下来的几年内被探测到。 因此，估计天空中强度的变化可能在改善我们对天体物理模型的理解中起到关键作用。 已经为所有先进地面干涉探测器的数据采集运行生成了天图。 虽然这些地图分别在像素和SpH基下生成，以探测局部和扩散的天体物理和宇宙学源，并且使用交叉相关作为共同策略的算法，但其底层代数和数值实现仍然不同。 因此，在这些分析中需要在两种基下生成天图。 我们表明，这些明显冗余的方法实际上可以统一为一种分析方法，该方法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这一点。 我们首先开发代数以表明两种不同基的结果可以轻松转换。 然后将这两种方案纳入现在标准的各向异性SGWB分析流程Pystoch中。 这将使SpH基下的SGWB各向异性搜索也能充分利用集成的\hpx 工具，并使在每个频率段进行搜索在计算上可行。 然而，我们采用不同的方法直接估计SpH矩。 我们表明，从这些不同方法中获得的结果非常吻合；SpH矩的差异小于$0.1$%，而Fisher信息矩阵的差异小于$0.01$%。 因此我们得出结论，一个天图就足以描述随机背景中的各向异性。 Pystoch的多种功能将有助于估计和限制表征各向异性背景的各种度量。 显示英文摘要

摘要： 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.