标题： 快速边缘化算法用于优化引力波探测、参数估计和天空定位 显示英文标题

标题： Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization

摘要： 我们引入了一种算法，用于对来自准圆形双星并合的引力波信号的似然函数进行边缘化，考虑了高次谐波和自旋引起的进动效应。 该算法以所有运行中的探测器中单个波形谐波与数据的匹配滤波时间序列以及谐波的协方差作为输入。 输出结果是在描述并合时间、位置和方向的外在参数上边缘化的高斯似然函数，以及这些参数的条件后验抽样。 我们的算法利用了波形对外在参数已知的解析依赖关系，通过一次波形评估高效地对其进行边缘化。 我们当前的实现可在单个CPU核心上在$\approx 50$ ms内达到边缘化似然函数10%的精度，并通过包`cogwheel`公开可用。 我们讨论了该工具在涉及高阶模式或进动的引力波搜索、高效且稳健的参数估计以及为引力波警报的电磁后续观测生成低延迟天空定位图方面的应用。 包含高次谐波可以提高距离测量的精度，相较于现有的低延迟定位方法具有优势。 显示英文摘要

摘要： We introduce an algorithm to marginalize the likelihood for a gravitational wave signal from a quasi-circular binary merger over its extrinsic parameters, accounting for the effects of higher harmonics and spin-induced precession. The algorithm takes as input the matched-filtering time series of individual waveform harmonics against the data in all operational detectors, and the covariances of the harmonics. The outputs are the Gaussian likelihood marginalized over extrinsic parameters describing the merger time, location and orientation, along with samples from the conditional posterior of these parameters. Our algorithm exploits the waveform's known analytical dependence on extrinsic parameters to efficiently marginalize over them using a single waveform evaluation. Our current implementation achieves a 10% precision on the marginalized likelihood within $\approx 50$ ms on a single CPU core and is publicly available through the package `cogwheel`. We discuss applications of this tool for gravitational wave searches involving higher modes or precession, efficient and robust parameter estimation, and generation of sky localization maps in low latency for electromagnetic followup of gravitational-wave alerts. The inclusion of higher modes can improve the distance measurement, providing an advantage over existing low-latency localization methods.