论文标题
LISA数据中的银河系二进制文件的贝叶斯参数估计与高斯过程回归
Bayesian parameter-estimation of Galactic binaries in LISA data with Gaussian Process Regression
论文作者
论文摘要
目前正在建造的激光干涉仪空间天线（LISA）旨在测量Milli-Hertz频带中的重力波信号。预计数以千计的银河二进制室将是观察到的重力波的主要来源。在MHz频率范围内产生信号的银河系二进制文件发射了准单色引力波，该波将通过Lisa不断测量。解决尽可能多的银河二进制文件是即将进行的LISA数据集分析的核心挑战。尽管据估计，数万这些重叠的重力波信号是可以解析的，其余的模糊成银河前景噪声。使用贝叶斯方法提取数万个信号在计算上仍然很昂贵。我们使用高斯工艺回归开发了一条新的端到端管道，以模拟对数似然函数，以迅速计算贝叶斯后分布。使用管道，我们能够解决由嘈杂数据组成的LISA数据挑战（LDC）1-3，以及重叠信号和尤其是微弱信号的其他挑战。
The Laser Interferometer Space Antenna (LISA), which is currently under construction, is designed to measure gravitational wave signals in the milli-Hertz frequency band. It is expected that tens of millions of Galactic binaries will be the dominant sources of observed gravitational waves. The Galactic binaries producing signals at mHz frequency range emit quasi monochromatic gravitational waves, which will be constantly measured by LISA. To resolve as many Galactic binaries as possible is a central challenge of the upcoming LISA data set analysis. Although it is estimated that tens of thousands of these overlapping gravitational wave signals are resolvable, and the rest blurs into a galactic foreground noise; extracting tens of thousands of signals using Bayesian approaches is still computationally expensive. We developed a new end-to-end pipeline using Gaussian Process Regression to model the log-likelihood function in order to rapidly compute Bayesian posterior distributions. Using the pipeline we are able to solve the Lisa Data Challenge (LDC) 1-3 consisting of noisy data as well as additional challenges with overlapping signals and particularly faint signals.
