Gravitational Wave Detection and Characterization

引力波探测和表征

• 批准号: 1306702
• 负责人: Neil Cornish
• 金额: $ 19.5万
• 依托单位: Montana State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1306702&HistoricalAwards=false
• 关键词: Gravitational; Wave; Detection; Characterization

The LIGO gravitational wave detectors are currently undergoing a major upgrade, with the goal of improving the broadband sensitivity by an order of magnitude. The scientific capabilities of the instruments can be further enhanced by improving the signal processing techniques used to tease faint gravitational wave signals out of the instrument noise. The research described in this proposal seeks to develop tools that will enhance our ability to detect and characterize transient gravitational wave signals. The initial searches for gravitational waves have taught us that non-Gaussian features in the data, such as noise transients or "glitches", impair our ability to detect weak signals. Experience with blind signal injections has taught us the importance of signal characterization and parameter estimation in assessing putative detections. Our goal is to develop and implement powerful new techniques that can help separate gravitational wave signals from instrument artifacts. This will be done in the framework of the BayesWave algorithm that was developed under a predecessor award. BayesWave provides a natural framework to develop targeted searches by incorporating prior knowledge about the signals and glitches. Multi-variate classifiers and cluster analysis tools will be used to identify glitch families based on the characterizations provided by the BayesWave analysis. These will aid in real-time detector studies and noise mitigation efforts. In the event of a detection, the signal characterization capability will help identify the type of astrophysical system that could have produced the observed waveform. These studies will become possible in near real-time using a new technique that speeds up the computation of the model likelihood by orders of magnitude. The detection of gravitational waves will allow a host of science questions to be addressed. Foremost among these will be whether the signals detected conform to the predictions of Einstein's theory of general relativity. The final component of this proposal deals with developing robust and generic techniques for identifying departures from general relativity using LIGO/Virgo data.The LIGO project presents young researchers and students with a wonderful opportunity to participate in the birth of a new observation science that is poised to make discoveries that will revolutionize astronomy and deliver unique insights into some of the Universe's most exotic phenomena. The research program outlined in this proposal offers tremendous opportunities for graduate and undergraduate students: the blend of creative activities associated with the development of sophisticated and innovative data analysis techniques, combined with hands on exposure to running existing search pipelines and working with production level computer code will provide excellent training for the next generation of gravitational wave astronomers. These skills are transferable and highly sought after in other fields: recent graduates from the Montana State gravitational wave astronomy group have found employment as national security intelligence analysts and in the medical research field of bioinformatics. Our group has been very active in bringing gravitational wave science to the public through talks, a school lecture program, and the production of a documentary. For this proposal we plan to produce new web-based educational resources that illustrate the signal processing and classification techniques used in our research.

LIGO引力波探测器目前正在进行重大升级，目标是将宽带灵敏度提高一个数量级。通过改进信号处理技术，从仪器噪声中提取微弱的引力波信号，可以进一步提高仪器的科学能力。本提案中描述的研究旨在开发工具，以增强我们探测和表征瞬态引力波信号的能力。对引力波的初步搜索告诉我们，数据中的非高斯特征，如瞬态噪声或“小故障”，会削弱我们探测微弱信号的能力。盲信号注入的经验告诉我们，信号表征和参数估计在评估假定检测中的重要性。我们的目标是开发和实现强大的新技术，可以帮助分离引力波信号和仪器伪像。这将在BayesWave算法的框架内完成，该算法是根据前辈奖开发的。BayesWave提供了一个自然的框架，通过整合有关信号和故障的先验知识来开发有针对性的搜索。多变量分类器和聚类分析工具将用于根据BayesWave分析提供的特征来识别故障家族。这将有助于实时探测器研究和降低噪音的努力。在探测的情况下，信号表征能力将有助于识别可能产生观测波形的天体物理系统的类型。使用一种新技术，这些研究将在接近实时的情况下成为可能，该技术可以将模型可能性的计算速度提高几个数量级。引力波的探测将使许多科学问题得以解决。其中最重要的是，探测到的信号是否符合爱因斯坦广义相对论的预测。该提案的最后一个组成部分涉及开发稳健和通用的技术，以识别使用LIGO/Virgo数据偏离广义相对论。LIGO项目为年轻的研究人员和学生们提供了一个绝佳的机会，让他们参与到一门新的观测科学的诞生中来，这门科学有望做出一些发现，这些发现将彻底改变天文学，并为宇宙中一些最奇特的现象提供独特的见解。本提案中概述的研究计划为研究生和本科生提供了巨大的机会：与复杂和创新数据分析技术发展相关的创造性活动，结合实际操作现有搜索管道和生产级计算机代码，将为下一代引力波天文学家提供出色的培训。这些技能是可转移的，在其他领域也很受欢迎：蒙大拿州引力波天文学小组最近的毕业生已经找到了国家安全情报分析师和生物信息学医学研究领域的工作。我们的团队一直非常积极地通过讲座、学校讲座项目和纪录片的制作，将引力波科学带给公众。对于这个提案，我们计划制作新的基于网络的教育资源，说明我们研究中使用的信号处理和分类技术。