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Coherent Bayesian inference on compact binary inspirals using a network of interferometric gravitational wave detectors

机译:利用干涉重力波检测器网络对紧凑型二元气旋的相干贝叶斯推断

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摘要

Presented in this paper is a Markov chain Monte Carlo (MCMC) routine for conducting coherent parameter estimation for interferometric gravitational wave observations of an inspiral of binary compact objects using data from multiple detectors. The MCMC technique uses data from several interferometers and infers all nine of the parameters (ignoring spin) associated with the binary system, including the distance to the source, the masses, and the location on the sky. The Metropolis-algorithm utilises advanced MCMC techniques, such as importance resampling and parallel tempering. The data is compared with time-domain inspiral templates that are 2.5 post-Newtonian (PN) in phase and 2.0 PN in amplitude. Our routine could be implemented as part of an inspiral detection pipeline for a world wide network of detectors. Examples are given for simulated signals and data as seen by the LIGO and Virgo detectors operating at their design sensitivity.
机译:本文介绍的是马尔可夫链蒙特卡洛(MCMC)例程,该例程用于使用多个检测器的数据对二元紧凑物体的吸气形干涉重力波观测进行相干参数估计。 MCMC技术使用来自多个干涉仪的数据,并推断与二进制系统相关的所有九个参数(忽略自旋),包括到源的距离,质量和天空位置。大都会算法利用了先进的MCMC技术,例如重要性重采样和并行回火。将数据与时域吸气模板进行比较,该模板的相位为牛顿后(PN)为2.5,幅度为2.0 PN。我们的例程可以作为全球检测器网络的吸气检测管道的一部分来实现。给出了以LIGO和处女座探测器在其设计灵敏度下工作的模拟信号和数据的示例。

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