论文标题
快速参数估计多中音二等合并的二进制合并估计
Fast Parameter Estimation of Binary Mergers for Multimessenger Followup
论文作者
论文摘要
大量的人类和观察资源专门用于晚期Ligo和处女座检测到的重力波事件的电磁随访。随着Ligo和处女座的敏感性的提高，检测到的来源速率将会提高。 Margalit＆Metzger（2019; Arxiv：1904.11995）建议，可能有必要优先考虑对未来事件的观察。最佳优先级需要快速测量重力波事件的质量和旋转，因为这些事件可以确定任何电磁发射的性质。我们扩展了Zackay等人的相对分成方法。 （2018; arxiv：1806.08792）to连贯的检测器网络统计量。我们表明，该方法可以从匹配过滤器搜索的输出中播种，并在贝叶斯参数测量框架中使用，以在32 CPU核心检测后20分钟内为源的参数产生边缘化的后验概率密度。我们证明，该算法会产生与使用标准重力波可能性运行参数估计的参数的无偏估计。我们鼓励在未来的Ligo-Virgo观察跑步中采用这种方法，以便快速传播检测事件的参数，以便观察社区可以充分利用其资源。
Significant human and observational resources have been dedicated to electromagnetic followup of gravitational-wave events detected by Advanced LIGO and Virgo. As the sensitivity of LIGO and Virgo improves, the rate of sources detected will increase. Margalit & Metzger (2019; arXiv:1904.11995) have suggested that it may be necessary to prioritize observations of future events. Optimal prioritization requires a rapid measurement of a gravitational-wave event's masses and spins, as these can determine the nature of any electromagnetic emission. We extend the relative binning method of Zackay et al. (2018; arXiv:1806.08792) to a coherent detector-network statistic. We show that the method can be seeded from the output of a matched-filter search and used in a Bayesian parameter measurement framework to produce marginalized posterior probability densities for the source's parameters within 20 minutes of detection on 32 CPU cores. We demonstrate that this algorithm produces unbiased estimates of the parameters with the same accuracy as running parameter estimation using the standard gravitational-wave likelihood. We encourage the adoption of this method in future LIGO-Virgo observing runs to allow fast dissemination of the parameters of detected events so that the observing community can make best use of its resources.
