标题： 各向同性引力波背景的成分分离 显示英文标题

标题： Component Separation of a Isotropic Gravitational Wave Background

摘要： 一个引力波背景（GWB）预计会在宇宙中存在，它是由大量无法分辨的天体物理源和早期宇宙现象叠加而成的。 背景的每一部分（例如，从原初度规扰动、双星黑洞、毫秒脉冲星等） 都有其特定的频谱形状。 许多正在进行的实验旨在探测不同频率范围内的GWB。 在过去二十年中，利用地基激光干涉引力波（GW）观测台的数据，在考虑单一频谱形状的情况下，在约50-1000 Hz的频率范围内设定了GWB的上限。 然而，一个强分量可以显著增强另一个分量的估计强度。 因此，应该联合估计具有不同频谱形状的分量的振幅。 在这里，我们提出了一种用于统计各向同性背景“分量分离”的方法，这种方法首次能够联合估计许多分量的振幅并设定上限。 该方法非常简单，并且几乎不需要计算。 它利用了测量值与实际分量振幅之间的线性关系，从而避免了基于抽样的方法（例如马尔可夫链蒙特卡洛（MCMC）或匹配滤波），这些方法在多维参数空间中计算密集且繁琐。 使用这种形式主义，我们还可以研究使用当前和即将出现的地基干涉仪检测器网络中的给定数据集可以分离出多少独立分量。 显示英文摘要

摘要： A Gravitational Wave Background (GWB) is expected in the universe from the superposition of a large number of unresolved astrophysical sources and phenomena in the early universe. Each component of the background (e.g., from primordial metric perturbations, binary neutron stars, milli-second pulsars etc.) has its own spectral shape. Many ongoing experiments aim to probe GWB at a variety of frequency bands. In the last two decades, using data from ground-based laser interferometric gravitational wave (GW) observatories, upper limits on GWB were placed in the frequency range of ~50-1000 Hz, considering one spectral shape at a time. However, one strong component can significantly enhance the estimated strength of another component. Hence, estimation of the amplitudes of the components with different spectral shapes should be done jointly. Here we propose a method for "component separation" of a statistically isotropic background, that can, for the first time, jointly estimate the amplitudes of many components and place upper limits. The method is rather straightforward and needs negligible amount of computation. It utilises the linear relationship between the measurements and the amplitudes of the actual components, alleviating the need for a sampling based method, e.g., Markov Chain Monte Carlo (MCMC) or matched filtering, which are computationally intensive and cumbersome in a multi-dimensional parameter space. Using this formalism we could also study how many independent components can be separated using a given dataset from a network of current and upcoming ground based interferometric detectors.