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

网络支持的发现和创新计划编号：0941417/0940924PIS：Pablo Laguna/Lee Finn研究所：佐治亚理工学院-佐治亚理工学院/宾夕法尼亚州立大学园区标题：CDI-类型I：合作研究：观测数据作为双星黑洞模拟的中央引擎由观测驱动的引力波天体物理学即将问世。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，这一新天文学的重大挑战之一是从隐藏在噪声数据中的信号中编码的信息中确定来源的特征。这项事业的成功程度将决定引力波观测能在多大程度上被用作发现的工具。拟议项目的动机是这样一个前提，即识别和解释通过引力波观测窗口收集的数据需要一套跨越几个学科的技能，从而为发展变革性和多学科研究并为这一引力波充当信使的新天文学的创新和进步打开大门。这个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中最重要的引力辐射源之一的逆问题，即双星黑洞系统的激发和合并。这一问题的解决对于利用广义相对论的预测能力和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用于同化观测数据的有效数值算法，应用确定性和随机参数估计技术来解决源特征问题。这项提议支持的活动旨在产生工具和方法，使数值模拟、引力波观测和用于引力物理以外的大型反问题的算法能够结合在一起。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、以及在计算贝叶斯推理理解方面的相关进展和计算资源池的结合而获得新的知识。在其更广泛的影响中，这个多学科团队打算开发一种使用数值相对论工具的新范式，以及一种创新的方法来解决引力物理中的逆问题。这种协作产生的工具将免费提供，并将直接适用于其他学科。这项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。学生将有机会积累数据分析、数值算法、高性能计算、优化和反问题方面的专业知识。

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.