脉冲星计时阵列中连续引力波探测和估计的研究

项目名称： 脉冲星计时阵列中连续引力波探测和估计的研究

项目编号： No.11503007

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 王炎

作者单位： 华中科技大学

项目金额： 23万元

中文摘要： 脉冲星计时阵列是目前最有前景的探测极低频（1 nHz-1 uHz）引力波的方法。这个频段的引力波波源主要是星系并合过程中形成的超大质量黑洞双星。随着数据量的快速增加，数值计算量和计算的不稳定性已成为制约数据处理的主要因素。本项目将以统计信号处理中的广义似然比检验为方法，研究脉冲星计时阵列中连续引力波信号的探测和参数估计问题，其主要内容包括（1）基于广义似然比检验建立引力波探测网络相干方法，分析在不同信噪比下的信号探测率和参数估计精度；（2）此种方法中存在的适定性问题的分析及其解决方法；（3）数据中存在多个连续单源信号时，各个信号的探测和分辨问题。本项目的研究可以提高连续引力波信号的探测率和相关参数的估计精度，增强算法的效率和稳定性。申请人有权得到NANOGrav组织的实际观测数据，并将本项目中研发的数据处理工具应用于其对引力波信号的分析之中。

中文关键词： 引力波；脉冲星计时；数据分析；超大质量黑洞双星

英文摘要： Pulsar timing array is a promising approach to detecting gravitational waves in the very low frequency range (1 nHz-1 uHz). The primary sources in such low frequencies are the supermassive black hole binaries which are formed in galactic mergers. With the rapid increase of data volume, computation power and numerical instability become the major limiting factors of data analysis. In this project, we propose to study the detection and parameter estimation based on the Generalized Likelihood Ratio Test for continuous gravitational wave signals in pulsar timing arrays, which includes (1) construct the detection statistic based on the Generalized Likelihood Ratio Test for the continuous gravitational waves, and study its detection probability and parameter estimation precision under different signal to noise ratios; (2) the posedness problem and its mitigation in this data analysis method; (3) the detection and resolution of multiple continuous source signals in the data. Research on this project can improve the detection probability and estimation accuracy of related parameters for continuous gravitational wave signals, and enhance the efficiency and stability of the algorithm. The applicant is entitled to obtain the actual observation data of the NANOGrav collaboration, and can apply the data processing tools developed in this project on the data to analyse of gravitational wave signals.

英文关键词： Gravitational wave;Pulsar timing;Data analysis;Supermassive black hole binary