MRI: Acquisition of a High-Capacity Data Analysis System for Gravitational-Wave Detection with Advanced LIGO

MRI：使用先进的 LIGO 获取用于引力波检测的大容量数据分析系统

• 批准号: 1626190
• 负责人: Patrick Brady
• 金额: $ 87.47万
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1626190&HistoricalAwards=false
• 关键词: MRI; Acquisition; Capacity; Data; Analysis

The Laser Interferometer Gravitational Wave Observatory (LIGO) is an NSF-funded project to detect gravitational waves, the ripples in space and time emitted by violent astrophysical events such as supernovae or colliding pairs of neutron stars or black holes. The first detection of these waves in September 2015 confirmed a central prediction of one the most fundamental theories in science: Einstein's theory of general relativity. With this and subsequent detections, LIGO is now operating as an astronomical observatory allowing us to explore the Universe using a completely new sense: gravitational waves. This award provides computing resources at UWM to analyze the LIGO data and will accelerate the discoveries enabled by the LIGO instruments. The computing cluster is designed to address the specific needs of Advanced LIGO data analysis. It is an integral part of the LIGO Scientific Collaboration (LSC) effort to analyze the full Advanced LIGO data sets. LIGO analysis activities will be prioritized on 90% of the cluster; other activities on the remaining 10% will include searches for radio pulsars/transients, the detection of gravitational waves using pulsar timing arrays by NANOGrav, and numerical astrophysics. Analyses to be run on the new cluster include searches for compact binaries and related parameter-estimation. The facility will be available to all 1000+ members of the LSC, and thus will have broad international impact. Moreover, these facilities have also been used by researchers at UWM and elsewhere for interdisciplinary research in fields such as astronomy and astrophysics. This will continue, thus ensuring that this instrument will have a broad scientific reach at UWM and beyond. The deployment and use of this instrument will also provide a hands-on opportunity for education and training to undergraduates, graduate students, post-doctoral scientists and academic staff in the use of large-scale computing hardware to tackle complex technical questions.The baseline computational instrument will consist of 720 execute nodes with 2,280 modern E3-1241v3 CPU cores. The group will add several high-memory login nodes, storage servers (1,152 TB gross storage) and networking equipment. Theoretical peak performance will be about 300 TFLOPS (AVX2) with sufficient storage to host all of the calibrated data from advanced detectors (30 TB/yr) and several months of raw data (600 TB/yr). Benchmarks indicate that the cluster will be able to process over 28 million template matches in real time. The scale of this project is commensurate with a new aspect of this mission within LIGO data analysis as a secondary site for running the real-time (latency of seconds) search for compact binary mergers -- a task best handled at a dedicated site such as UWM. This instrument will facilitate and participate the discovery and the study of the exotic astrophysical events throughout the Universe that produce gravitational waves. This award will provide part of the computational resources needed to analyze the data as it is delivered by the new Advanced LIGO instruments. It will support an array of scientific activities including the detection of new gravitational-wave signals and the broader astrophysical and astronomical follow-on analyses.

激光干涉引力波天文台（LIGO）是一个由NSF资助的项目，用于探测引力波，即由超新星或中子星或黑洞碰撞对等剧烈天体物理事件发出的空间和时间涟漪。2015年9月首次探测到这些波，证实了科学中最基本的理论之一的核心预测：爱因斯坦的广义相对论。通过这次和随后的探测，LIGO现在作为一个天文观测站运行，允许我们使用一种全新的感觉来探索宇宙：引力波。该奖项为UWM提供计算资源，以分析LIGO数据，并将加速LIGO仪器的发现。计算集群旨在满足高级LIGO数据分析的特定需求。它是LIGO科学合作组织（LSC）分析完整的高级LIGO数据集的一个组成部分。LIGO分析活动将优先考虑90%的集群;其余10%的其他活动将包括搜索射电望远镜/瞬变，NANOGrav使用脉冲星计时阵列探测引力波，以及数值天体物理学。 分析将运行在新的集群包括搜索紧凑的二进制和相关的参数估计。该设施将提供给LSC的所有1000多个成员，因此将产生广泛的国际影响。此外，这些设施也被UWM和其他地方的研究人员用于天文学和天体物理学等领域的跨学科研究。 这将继续下去，从而确保这一工具将有一个广泛的科学范围在UWM和超越。该仪器的部署和使用还将为本科生、研究生、博士后科学家和学术人员提供使用大规模计算硬件解决复杂技术问题的教育和培训的实践机会。基线计算仪器将由720个执行节点组成，配备2，280个现代E3- 1241 v3 CPU内核。该集团将增加几个高内存登录节点，存储服务器（1,152 TB总存储）和网络设备。 理论峰值性能约为300 TFLOPS（AVX 2），具有足够的存储空间，可容纳来自高级探测器的所有校准数据（30 TB/年）和数月的原始数据（600 TB/年）。 基准测试表明，该集群将能够真实的处理超过2800万个模板匹配。该项目的规模与LIGO数据分析中的这一使命的新方面相称，作为运行实时（秒级延迟）搜索紧凑二元合并的辅助站点-最好在UWM等专用站点处理任务。该仪器将促进和参与发现和研究整个宇宙中产生引力波的奇异天体物理事件。该奖项将提供分析新的Advanced LIGO仪器提供的数据所需的部分计算资源。它将支持一系列科学活动，包括探测新的引力波信号和更广泛的天体物理学和天文学后续分析。

项目成果

The NANOGrav 11 yr Data Set: Limits on Gravitational Wave Memory

• DOI: 10.3847/1538-4357/ab6083
• 发表时间: 2019-11
• 期刊: The Astrophysical Journal
• 作者: K. Aggarwal;Z. Arzoumanian;P. T. Baker;P. T. Baker;A. Brazier;P. Brook;S. Burke-Spolaor;S. Chatterjee;J. Cordes;N. Cornish;F. Crawford;H. Cromartie;K. Crowter;M. DeCesar;P. Demorest;T. Dolch;J. Ellis;J. Ellis;R. Ferdman;E. Ferrara;E. Fonseca;N. Garver-Daniels;P. Gentile;D. Good;J. Hazboun;A. M. Holgado;E. Huerta;K. Islo;R. Jennings;G. Jones;M. Jones;D. Kaplan;L. Kelley;J. Key;M. Lam;T. Lazio;L. Levin;D. Lorimer;J. Luo;R. Lynch;D. Madison;M. McLaughlin;S. McWilliams;C. Mingarelli;C. Ng;D. Nice;T. Pennucci;N. Pol;S. Ransom;S. Ransom;P. Ray;X. Siemens;X. Siemens;J. Simon;R. Spiewak;R. Spiewak;I. Stairs;D. Stinebring;K. Stovall;J. Swiggum;S. Taylor;S. Taylor;M. Vallisneri;R. V. Haasteren;S. Vigeland;C. Witt;Wenbai Zhu

Modeling the Uncertainties of Solar System Ephemerides for Robust Gravitational-wave Searches with Pulsar-timing Arrays

• DOI: 10.3847/1538-4357/ab7b67
• 发表时间: 2020-01
• 期刊: The Astrophysical Journal
• 作者: M. Vallisneri;Stephen R. Taylor;Stephen R. Taylor;J. Simon;W. Folkner;Ryan S. Park;Curt Cutler;Justin A. Ellis;T. Lazio;S. Vigeland;K. Aggarwal;Z. Arzoumanian;P. T. Baker;P. T. Baker;A. Brazier;P. Brook;S. Burke-Spolaor;S. Chatterjee;J. Cordes;N. Cornish;F. Crawford;H. Cromartie;K. Crowter;M. DeCesar;P. Demorest;T. Dolch;R. Ferdman;E. Ferrara;E. Fonseca;N. Garver-Daniels;P. Gentile;D. Good;J. Hazboun;A. M. Holgado;E. Huerta;K. Islo;R. Jennings;G. Jones;Megan L. Jones;D. Kaplan;L. Z. Kelley;J. Key;M. Lam;L. Levin;D. Lorimer;Jing Luo;R. Lynch;D. Madison;M. McLaughlin;S. McWilliams;C. Mingarelli;C. Ng;D. Nice;T. Pennucci;N. Pol;S. Ransom;S. Ransom;P. Ray;X. Siemens;X. Siemens;R. Spiewak;R. Spiewak;I. Stairs;D. Stinebring;K. Stovall;J. Swiggum;R. V. Haasteren;C. Witt;Weiwei Zhu

GW190814: Gravitational Waves from the Coalescence of a 23 Solar Mass Black Hole with a 2.6 Solar Mass Compact Object

• DOI: 10.3847/2041-8213/ab960f
• 发表时间: 2020-06-01
• 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.

A Machine Learning-based Source Property Inference for Compact Binary Mergers

• DOI: 10.3847/1538-4357/ab8dbe
• 发表时间: 2020-06-01
• 期刊: ASTROPHYSICAL JOURNAL
• 影响因子: 4.9
• 作者: Chatterjee, Deep;Ghosh, Shaon;Pannarale, Francesco
• 通讯作者: Pannarale, Francesco

A self-consistent method to estimate the rate of compact binary coalescences with a Poisson mixture model

• DOI: 10.1088/1361-6382/ab5f2d
• 发表时间: 2019-03
• 期刊: Classical and Quantum Gravity
• 影响因子: 3.5
• 作者: S. Kapadia;S. Caudill;J. Creighton;W. Farr;G. Mendell;A. Weinstein;K. Cannon;H. Fong;P. Godwin;R. K. Lo;R. Magee;D. Meacher;C. Messick;S. Mohite;D. Mukherjee;S. Sachdev