LIGO Data Analysis, Detector Characterization and Gravitational Wave Astronomy

LIGO 数据分析、探测器表征和引力波天文学

• 批准号: 0099559
• 负责人: Lee Finn
• 金额: $ 65.08万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0099559&HistoricalAwards=false
• 关键词: LIGO; Data; Analysis; Detector; Characterization

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.

这项建议涉及引力波天文学的LIGO数据分析方面的研究。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。这项被称为“数据调节”的工作对LIGO的成功至关重要，因为引力波信号预计会很弱，很可能被仪器噪声完全掩盖。提出了几种调整LIGO数据的方法，包括使用“人工智能”工具来处理时间顺序的数据。此外，LIGO数据将被分析为1.设置已知脉冲星引力波强度的上限，2.搜索意外的引力波来源，3.将合成的三个LIGO探测器的数据合并为一个探测器。识别数据流中的引力波信号的技术涉及使用旨在挑选出具有特定特征的信号的过滤器。提出了几种创新的方法，用于在存在非高斯噪声的情况下开发非线性滤波器，包括使用技术来确定当数据分布的非正态矩已知时使用的最合适的似然函数。