Search for a stochastic gravitational-wave signal in the second round of the Mock LISA Data Challenges

被引:13
|
作者
Robinson, E. L. [1 ]
Romano, J. D. [2 ,3 ,4 ]
Vecchio, A. [1 ]
机构
[1] Univ Birmingham, Sch Phys & Astron, Birmingham B15 2TT, W Midlands, England
[2] Cardiff Univ, Sch Phys & Astron, Cardiff CF24 3AA, S Glam, Wales
[3] Univ Texas Brownsville, Dept Phys & Astron, Brownsville, TX 78520 USA
[4] Univ Texas Brownsville, Ctr Gravitat Wave Astron, Brownsville, TX 78520 USA
基金
英国科学技术设施理事会;
关键词
D O I
10.1088/0264-9381/25/18/184019
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
The analysis method currently proposed to search for isotropic stochastic radiation with the Laser Interferometer Space Antenna (LISA) relies on the combined use of two LISA channels, one of which is insensitive to gravitational waves, such as the symmetrized Sagnac. For this method to work, it is essential to know how the instrumental noise power in the two channels are related to one another; however, no quantitative estimates of this key information are available to date. The purpose of our study is to assess the performance of the symmetrized Sagnac method for different levels of prior information regarding the instrumental noise. We develop a general approach in the framework of Bayesian inference and an end-to-end analysis algorithm based on Markov chain Monte Carlo methods to compute the posterior probability density functions of the relevant model parameters. We apply this method to data released as part of the second round of the Mock LISA Data Challenges. For the selected (and somewhat idealized) example cases considered here, we find that for a signal whose amplitude dominates the instrumental noise by a factor approximate to 25, a prior uncertainty of a factor approximate to 2 in the ratio between the power of the instrumental noise contributions in the two channels allows for the detection of isotropic stochastic radiation. More importantly, we provide a framework for more realistic studies of LISA's performance and development of analysis techniques in the context of searches for stochastic signals.
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页数:10
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