Robust Detection and Characterization of Transient Gravitational Waves

瞬态引力波的稳健检测和表征

• 批准号: 1506439
• 负责人: Peter Jenke
• 金额: $ 19.83万
• 依托单位: University of Alabama in Huntsville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1506439&HistoricalAwards=false
• 关键词: Robust; Detection; Characterization; Transient; Gravitational

Hardware upgrades to the Advanced LIGO observatories are complete and soon the most sensitive gravitational wave (GW) detectors the world has ever seen will begin collecting data. Sophisticated data analysis methods are needed to take full advantage of the improved sensitivity and make GW astronomy a reality. Previous analyses have used simplifying assumptions about the detector noise, data calibration, and in some cases the GW signal itself, hindering our ability to make confident detection claims or accurately infer the nature of a GW signal. The research enabled by this award will advance GW data analysis by improving our ability to detect and characterize transient astrophysical signals. The broad theme of the work supported by this award is to build a detailed model of LIGO data including instrument noise, calibration uncertainty, and GW signals. The resulting data analysis methods will improve detection efficiency and reduce systematic errors in parameter estimation. This award will also allow the PI to continue participating in impactful education and public outreach activities and to do so in a region of the United States which has little exposure to gravitational physics. Outreach activities related to this award will include interactions with area high schools, colleges, and universities to publicize the exciting science being done by Advanced LIGO. Research supported by this award will advance three main topics in LIGO data analysis: (i) Un-modeled gravitational wave signals present a challenge for detection and parameter estimation because the signals are rare while transient noise events, or 'glitches', are common. We will complete initial development and then continue to upgrade the BayesWave and BayesLine data analysis pipelines, which together are the state-of-the-art method for distinguishing between burst signals and glitches and drawing inferences about physical properties of the GW source. (ii) The breakthrough technology used by BayesWave and BayesLine is a realistic model for the LIGO noise. The deployment of this model is most mature in the burst analysis; however, all GW searches compete with the same instrument noise. We will adapt the BayesLine algorithm to work with the parameter estimation pipeline, LALInference, for compact binary coalescence signals. (iii) The LIGO data must be calibrated, and there are small statistical uncertainties in amplitude and phase as a result. Previous source characterization analyses have not incorporated uncertainty in the calibration in their data analysis method. The BayesLine algorithm provides the foundation for modeling calibration errors and will be modified to account for systematic phase and amplitude uncertainty in compact binary and burst parameter estimation. This award is supported by the LIGO Research Program of Physics Division in collaboration with the EPSCoR program of the Office of International and Integrative Activities.

高级LIGO天文台的硬件升级已经完成，很快世界上最灵敏的引力波探测器将开始收集数据。需要先进的数据分析方法来充分利用提高的灵敏度，使GW天文学成为现实。先前的分析简化了对探测器噪声、数据校准以及在某些情况下GW信号本身的假设，阻碍了我们做出自信的检测声明或准确推断GW信号性质的能力。这项研究将通过提高我们探测和表征瞬态天体物理信号的能力来推进GW数据分析。该奖项支持的工作的主要主题是建立一个详细的LIGO数据模型，包括仪器噪声、校准不确定性和GW信号。由此产生的数据分析方法将提高检测效率，减少参数估计的系统误差。该奖项还将允许PI继续参与有影响力的教育和公共宣传活动，并在美国很少接触引力物理学的地区这样做。与该奖项相关的推广活动将包括与当地高中、学院和大学的互动，以宣传先进LIGO正在进行的令人兴奋的科学研究。该奖项支持的研究将推进LIGO数据分析的三个主要主题：(i)未建模的引力波信号对探测和参数估计提出了挑战，因为信号很少，而瞬态噪声事件或“故障”很常见。我们将完成初始开发，然后继续升级BayesWave和BayesLine数据分析管道，它们一起是最先进的方法，用于区分突发信号和故障，并对GW源的物理特性进行推断。（ii） BayesWave和BayesLine使用的突破性技术是LIGO噪声的现实模型。该模型在突发分析中应用最为成熟；然而，所有的GW搜索都与相同的仪器噪声竞争。我们将调整BayesLine算法与参数估计管道LALInference一起工作，用于紧凑的二进制合并信号。（iii） LIGO数据必须经过校准，因此在振幅和相位上存在较小的统计不确定性。以前的源表征分析在其数据分析方法中没有纳入校准中的不确定度。BayesLine算法为建模校准误差提供了基础，并将进行修改，以考虑紧凑二进制和突发参数估计中系统相位和幅度的不确定性。该奖项由物理部的LIGO研究计划与国际和综合活动办公室的EPSCoR计划合作支持。