CDI-Type I: Collaborative ResearchL Observational Data as Central Engine of Binary Black Home Simulations

CDI-I 型：协作研究L 观测数据作为二元黑色家庭模拟的中心引擎

• 批准号: 0940924
• 负责人: Lee Finn
• 金额: $ 15.5万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0940924&HistoricalAwards=false
• 关键词: CDI; Type; Collaborative; ResearchL; Observational

Cyber-Enabled Discovery and Innovation (CDI)Proposal Number: 0941417 / 0940924PIs: Pablo Laguna / Lee FinnInstitutions: Georgia Tech Research Corporation - Georgia Institute of Technology / Pennsylvania State Univ University ParkTitle: CDI-Type I: Collaborative Research: Observational Data as Central Engine of Binary Black Hole SimulationsA gravitational wave astrophysics driven by observations is just around the corner. Interferometric gravitational-wave detectors such as LIGO and its partners have reached design sensitivity. The general consensus is that first detections will take place in the very near future. Beyond detection, one of the grand challenges in this new astronomy is the characterization of sources from the information encoded in the signals buried in the noisy data. The degree of success in this enterprise will determine the extent to which gravitational wave observations can be used as a tool of discovery. The proposed project is motivated by the premise that recognizing and interpreting the data collected through the window of gravitational wave observations requires a set of skills spanning several disciplines, thus presenting an opportunity to develop transformative and multidisciplinary research and to open the door to innovations and advances in this new astronomy, in which gravitational waves act as messengers. The central theme of this project is using numerical-relativity tools and data-analysis methodologies to solve the inverse problem in gravitational physics for one of the most important sources of gravitational radiation, the inspiral and merger of a binary black hole system. The solution to this problem is essential for harnessing the predictive power of general relativity and for enhancing the conversation between the data collected by gravitational wave interferometric detectors and the questions posed by astrophysics. The project approach is development of efficient numerical algorithms for assimilating observational data, applying deterministic and stochastic parameter estimation techniques to address the source characterization problem. Activities supported by this proposal are intended to produce tools and methodologies that enable the marriage of numerical simulation, gravitational wave observation and algorithms used in large inverse problems outside gravitational physics. These tools will enable acquiring new knowledge through the analysis of large and rich multi-spectral gravitational observations, multi-scale numerical relativity simulations, catalyzed by the combination of the still-growing pool of computational resources and related advances in the understanding of computational Bayesian inference. Among its broader impacts, this multidisciplinary team intends to develop a new paradigm for using numerical relativity tools and an innovative approach to the inverse problem in gravitational physics. The tools produced by this collaboration will be made freely available and will be directly applicable to other disciplines. The proposed work exploits algorithms and methodology to tackle inverse problems in areas such as global climate change, weather forecasting, medical imaging and reservoir simulations. Students will have opportunities to build expertise in data analysis, numerical algorithms, high-performance computing, optimization, and inverse problems.

网络使能的发现和创新（CDI）提案编号：0941417 /0940924 PI：巴勃罗拉古纳/李芬机构：格鲁吉亚技术研究公司-格鲁吉亚理工学院/宾夕法尼亚州立大学公园标题：CDI-I型：合作研究：观测数据作为二元黑洞模拟的中心引擎一个由观测驱动的引力波天体物理学即将到来。干涉引力波探测器，如LIGO及其合作伙伴，已经达到了设计灵敏度。普遍的共识是，第一次探测将在不久的将来进行。除了探测之外，这个新天文学的一个重大挑战是从隐藏在噪声数据中的信号中编码的信息中表征源。这项事业的成功程度将决定引力波观测在多大程度上可以用作发现的工具。该拟议项目的动机是，认识和解释通过引力波观测窗口收集的数据需要一套跨越多个学科的技能，从而为发展变革性和多学科研究提供了机会，并为创新和进步打开了大门在这种新的天文学中，引力波充当信使。该项目的中心主题是使用数值相对论工具和数据分析方法来解决引力物理学中最重要的引力辐射源之一的逆问题，即双黑洞系统的螺旋和合并。这个问题的解决方案对于利用广义相对论的预测能力以及加强引力波干涉探测器收集的数据与天体物理学提出的问题之间的对话至关重要。 该项目的方法是开发有效的数值算法，用于同化观测数据，应用确定性和随机参数估计技术来解决源表征问题。这项建议所支持的活动旨在产生工具和方法，使数值模拟、引力波观测和引力物理学以外的大型逆问题所用算法能够结合起来。这些工具将能够通过分析大量和丰富的多光谱引力观测、多尺度数值相对论模拟来获得新的知识，这些都是由仍在增长的计算资源库和在理解计算贝叶斯推理方面的相关进展所催化的。在其更广泛的影响中，这个多学科团队打算开发一种新的范式，用于使用数值相对论工具和一种创新的方法来解决引力物理学中的逆问题。这项合作产生的工具将免费提供，并将直接适用于其他学科。拟议的工作利用算法和方法来解决全球气候变化，天气预报，医学成像和水库模拟等领域的逆问题。学生将有机会建立在数据分析，数值算法，高性能计算，优化和逆问题的专业知识。