LIGO Data Analysis, Detector Characterization and Gravitational Wave Astronomy

该提案涉及LIGO数据分析的研究，导致引力波天文学。 特别是，将开发从LIGO数据流中去除仪器噪声的方法，为其分析做准备。 这项工作被称为“数据调节”，对LIGO的成功至关重要，因为引力波信号预计很弱，很可能被仪器噪声完全掩盖。 提出了几种方法来调节LIGO数据，包括使用“人工智能”工具的时间顺序数据。 此外，LIGO数据将被分析为1。设定已知脉冲星引力波强度的上限，2.寻找未预料到的引力波源，3.联合收割机将来自三个LIGO探测器的数据合成为一个探测器。在数据流中识别引力波信号的技术涉及使用滤波器，旨在挑选出具有特定特征的信号。 提出了几种创新的方法，在非高斯噪声的存在下，非线性滤波器的发展，涉及使用的技术，以确定最合适的似然函数时使用的数据分布的非正态矩是已知的。

This proposal addresses research in LIGO data analysis leading to gravitational wave astronomy. In particular, methods for removing instrumental noise from the LIGO data stream, preparatory to its analysis will be developed. This work, called "data conditioning", is critical to the success of the LIGO since the gravitational wave signals are expected to be weak and would most likely be totally masked by instrumental noise. Several methods are proposed for conditioning LIGO data including the use of "artificial intelligence" tools for time-ordered data. In addition, LIGO data will be analysed to 1. set upper limits on the gravitational wave strength from known pulsars,2. search for unanticipated sources of gravitational waves,3. combine data from the three LIGO detectors synthesized to perform as a single detector.On technique for identifying gravitational wave signals in the data stream invloves the use of filters designed to pick out a signal with specific characteristics. Several innovative methods are proposed for the development of non-linear filters in the presence of non-Gaussian noise, involving the use of techniques to determine the most appropriate likelihood function for use when non-normal moments of the data distribution are known.