Framework: An A+ Framework for Multimessenger Astrophysics Discoveries through Real-Time Gravitational Wave Detection

框架：通过实时引力波探测进行多信使天体物理学发现的框架

• 批准号: 2103662
• 负责人: Chad Hanna
• 金额: $ 339.75万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103662&HistoricalAwards=false
• 关键词: Framework; A+; Multimessenger; Astrophysics; Discoveries

The first direct detection of ripples in space, known as gravitational waves, by the NSF-funded LIGO (Laser Interferometer Gravitational-wave Observatory) project in 2015 opened a new window on the universe and provided an unprecedented ability to study distant astronomical phenomena that could otherwise not be seen with conventional telescopes. The subsequent 2017 detection of merging neutron stars, through gravitational waves with LIGO combined with the light detected by conventional telescopes, opened a new era whereby scientists hope to routinely study the universe using information analogous to both sight and sound. This project will directly enable future detections of gravitational waves through the development of robust signal processing software and an ecosystem of cyberinfrastructure services designed to analyze LIGO data in real time. The program will involve a diverse group of undergraduate students, graduate students, postdoctoral researchers, computational scientists, and faculty in transformative science. This work contributes to the national cyberinfrastructure as a core data-producing component for astronomy and will be relied upon by thousands of scientists globally as they progress the state of knowledge through the study of black holes, neutron stars, fundamental physics, and the evolution of the Universe.With the goal of making new coincident gravitational-wave and electromagnetic observations commonplace, this project targets the development of a software framework for the real-time discovery of gravitational waves with the world-wide network of gravitational-wave detectors including LIGO, Virgo, and KAGRA. With this project, the investigators intend to provide a sustainable community-driven framework supporting current gravitational-wave detectors while developing new infrastructure for the LIGO A+ upgrade in about 2025. The team will develop a real-time gravitational wave processing framework around the following themes: 1) accelerating the pace of discovery and dissemination of results, 2) advancing the use of machine learning and artificial intelligence in production gravitational-wave astronomy, 3) improving scientific robustness and reproducibility, and 4) increasing adoption of the developed software and services. The framework will be used to create libraries, applications and services for real-time calibrated strain data, real-time data quality information and a quick-response gravitational-wave search for merging neutron stars and black holes, all of which will culminate in daily gravitational-wave discoveries released publicly. This framework will contribute gravitational-wave discovery services operating in a high-availability mode with the goal of greater than 99% uptime. A host of scientific metrics will be developed into a real-time test infrastructure to ensure that gravitational-wave alerts are accurate and robust throughout software development and release cycles. This research will have a far-reaching impact on several scientific disciplines with new gravitational-wave and multi-messenger astrophysics discoveries and it will impact society through a gradual change in the shared knowledge about the universe. Beyond these general societal impacts, the project personnel intend to directly weave training and participation broadening activities into their research through 1) training and broadening participation in the research community via quarterly workshops, 2) providing professional development opportunities for the project personnel through training seminars, and 3) engaging and educating the next generation of scientists in the geographic community with a summer school for high school students.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Windows on the Universe NSF Big Idea program, the Physics at the Information Frontier (PIF) program in the Division of Physics (PHY), and the Division of Astronomical Sciences (AST).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF资助的Ligo（激光干涉仪重力波观测站）在2015年首次直接检测空间中的波纹，称为引力波，称为引力波，在宇宙上打开了一个新的窗口，并提供了一种前所未有的能力，可以研究远处的天文现象，否则可以在传统望远镜上看到这种现象。随后的2017年发现中子恒星通过引力波与Ligo结合了传统望远镜检测到的光，开辟了一个新时代，科学家希望使用类似于视觉和声音的信息来定期研究宇宙。该项目将通过开发可靠的信号处理软件和旨在实时分析LIGO数据的网络基础结构服务的生态系统来直接实现重力波的未来检测。该计划将涉及多样化的本科生，研究生，博士后研究人员，计算科学家以及变革科学的教职员工。 This work contributes to the national cyberinfrastructure as a core data-producing component for astronomy and will be relied upon by thousands of scientists globally as they progress the state of knowledge through the study of black holes, neutron stars, fundamental physics, and the evolution of the Universe.With the goal of making new coincident gravitational-wave and electromagnetic observations commonplace, this project targets the development of a software framework for the real-time通过包括Ligo，处女座和Kagra在内的全球引力波检测器网络发现引力波。通过该项目，调查人员打算在2025年左右为Ligo A+升级开发新的基础设施时提供一个可持续的社区驱动框架，以支持当前的重力波检测器。该团队将围绕以下主题开发一个实时的重力浪潮处理框架：1）加速了范围的范围，并加速了研究结果的范围，并加速了研究结果的范围，并逐步研究了2），并在2次中进行了研究，并逐步研究了2）。天文学，3）提高科学鲁棒性和可重复性，4）增加了开发的软件和服务的采用。该框架将用于创建用于实时校准应变数据，实时数据质量信息的库，应用程序和服务，以及快速响应引力波搜索合并中子星和黑洞，所有这些都将在每日的重力波发现中公开发布。该框架将在高可用性模式下贡献重力波发现服务，其目标超过99％。许多科学指标将被发展为实时测试基础架构，以确保在软件开发和释放周期中，重力波警报是准确且健壮的。 这项研究将对具有新的引力波和多理智的天体物理学发现的几个科学学科产生深远的影响，并通过对宇宙的共同知识的逐步改变来影响社会。除了这些一般的社会影响之外，项目人员还打算通过培训来直接编织培训和参与将活动扩展到他们的研究中。宇宙NSF大创意计划的Windows，物理学部（PHY）的信息前沿（PIF）计划以及天文学科学部（AST）的窗口。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力和更广泛影响的评估来评估通过评估的支持，并被认为是值得的。

项目成果

Improving LIGO calibration accuracy by using time-dependent filters to compensate for temporal variations

通过使用瞬态滤波器补偿时间变化来提高 LIGO 校准精度

• DOI: 10.1088/1361-6382/acabf6
• 发表时间: 2023
• 期刊: Classical and Quantum Gravity
• 影响因子: 3.5
• 作者: Wade, M;Viets, A D;Chmiel, T;Stover, M;Wade, L
• 通讯作者: Wade, L

Bayesian parameter estimation for targeted anisotropic gravitational-wave background

• DOI: 10.1103/physrevd.107.023024
• 发表时间: 2022-08
• 期刊: Physical Review D
• 影响因子: 5
• 作者: Leo Tsukada;S. Jaraba;D. Agarwal;E. Floden

Observation of Gravitational Waves from Two Neutron Star-Black Hole Coalescences

• DOI: 10.3847/2041-8213/ac082e
• 发表时间: 2021-07-01
• 期刊: ASTROPHYSICAL JOURNAL LETTERS
• 影响因子: 7.9
• 作者: Abbott, R.;Abbott, T. D.;Zweizig, J.
• 通讯作者: Zweizig, J.

Metric assisted stochastic sampling search for gravitational waves from binary black hole mergers

• DOI: 10.1103/physrevd.106.084033
• 发表时间: 2021-10
• 期刊: Physical Review D
• 影响因子: 5
• 作者: C. Hanna;P. Joshi;R. Huxford;K. Cannon;S. Caudill;C. Chan;B. Cousins;J. Creighton;B. Ewing;Miguel Fernandez;H. Fong;P. Godwin;R. Magee;D. Meacher;C. Messick;S. Morisaki;D. Mukherjee;H. Ohta;A. Pace;S. Privitera;S. Sachdev;S. Sakon;Divya Singh;R. Tapia;L. Tsukada;D. Tsuna;T. Tsutsui;K. Ueno;A. Viets;L. Wade;M. Wade;Jonathan Wang