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Building a stochastic template bank for detecting massive black hole binaries

机译:建立随机模板库以检测大量黑洞二进制文件

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摘要

The coalescence of pairs of massive black holes are the strongest and most promising sources for LISA. In fact, the gravitational wave signal from the final inspiral and merger will be detectable throughout the universe. In this paper we describe the first stEP 1in a two-step hierarchical search for the gravitational wave signal from inspiraling massive BH binaries. It is based on a method routinely used in ground-based gravitational wave astronomy, namely filtering the data through a bank of templates. However we use a novel, Monte Carlo based (stochastic), method for laying a grid in the parameter space, and we use the likelihood maximized analytically over some parameters, known as the F-statistic, as a detection statistic. We build a coarse template bank to detect gravitational wave signals and to make preliminary parameter estimation. The best candidates will be followed up using a Metropolis-Hasting stochastic search to refine the parameter estimates. We demonstrate the performance of the method by applying it to the Mock LISA data challenge 1B (training data set).
机译:成对的大量黑洞是LISA的最强和最有希望的来源。实际上,来自最终吸气和合并的引力波信号将在整个宇宙中被检测到。在本文中,我们描述了第一个stEP 1,该过程分两步进行分层搜索,从大量BH二进制文件中吸取引力波信号。它基于地面重力波天文学中通常使用的一种方法,即通过一组模板过滤数据。但是,我们使用一种新颖的基于蒙特卡洛的(随机的)方法在参数空间中放置网格,并且我们将某些参数(称为F统计量)在分析上最大化的似然性用作检测统计量。我们建立一个粗略的模板库,以检测重力波信号并进行初步的参数估计。最佳候选者将使用Metropolis-Hasting随机搜索进行跟踪,以优化参数估计。我们通过将其应用于模拟LISA数据质询1B(训练数据集)来演示该方法的性能。

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