MRI: Acquisition of a Computing System for Large Simulation Data Sets in Multimessenger Astrophysics

MRI：获取多信使天体物理学中大型模拟数据集的计算系统

• 批准号: 2018420
• 负责人: Manuela Campanelli
• 金额: $ 23万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018420&HistoricalAwards=false
• 关键词: MRI; Acquisition; Computing; System; Large

This award supports research in relativity and relativistic astrophysics and it addresses the priority area of NSF's "Windows on the Universe" Big Idea. Astrophysics is currently undergoing an unprecedented transformation with the advent of multimessenger astronomy, which in turn is driven by the recent detection of gravitational waves from several black hole neutron star mergers, and by the avalanche of astronomical data that is now and will soon be collected by a range of powerful new and forthcoming facilities. Mergers of compact binary systems including neutron stars and black-holes of all mass ranges encode information about fundamental physics, demonstrate the behavior of matter in the most extreme environments in the Universe, give insights into the formation, acceleration and dissipation of the most powerful particle emission processes, and provide missing links in our understanding of the end stages of stellar evolution and of their astrophysical origin. The equipment proposed here will enable research in a broad range of research projects aimed at answering some the key unresolved questions about these mergers by providing key resources needed to analyze the huge data sets coming from large scale state-of-the-art simulations performed at the NSF's national supercomputers facilities. This new system will also allow the team to host a public data repository to share their results with the larger scientific community, useful for supporting further investigations, such as predicted multimessenger light curves, snapshots of ejecta structure at selected times, suitable for use as initial conditions for longer-term simulations, and tracer-particle trajectories useful for further nucleosynthesis calculations. These datasets will also be relevant for the interpretations of observations by current ground-based detectors, such as advanced LIGO, and similar detectors across the globe, future space-based gravitational wave missions, such as LISA, and upcoming major astronomical observatories such as The Vera Rubin's Large Synoptic Survey Telescope (LSST).This award supports the acquisition of a new data storage and analysis cluster to support multimessenger astrophysics. The system will consist of 16 analysis nodes and 3 petabytes of online storage, to be hosted at the Rochester Institute of Technology (RIT). The proposed system will enable the RIT group and their collaborators to store and analyze very large data sets produced by simulations carried out in both local computing clusters and at national supercomputing facilities, such as the Frontera exascale system. The new system will enable highly accurate modeling of compact objects that are key sources of gravitational waves, such as black hole and neutron star binaries and supermassive black hole binaries. These simulations often require years of post-simulation analysis to extract the relevant physics due to their complexity and size.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.

该奖项支持相对论和相对论天体物理学的研究，并涉及美国国家科学基金会“宇宙之窗”大创意的优先领域。随着多信使天文学的出现，天体物理学目前正在经历前所未有的转变，而多信使天文学的出现又是由最近检测到的几次黑洞中子星合并产生的引力波以及现在和即将收集的大量天文数据推动的。一系列强大的新设施和即将推出的设施。 包括中子星和所有质量范围黑洞在内的紧凑双星系统的合并编码了有关基础物理的信息，展示了宇宙中最极端环境中物质的行为，深入了解最强大粒子的形成、加速和耗散发射过程，并为我们理解恒星演化的最终阶段及其天体物理起源提供了缺失的环节。 这里提出的设备将支持广泛的研究项目的研究，旨在通过提供分析来自大规模最先进模拟的巨大数据集所需的关键资源来回答有关这些合并的一些关键的未解决问题。 NSF 的国家超级计算机设施。这个新系统还将允许团队托管一个公共数据存储库，以便与更大的科学界分享他们的结果，这有助于支持进一步的研究，例如预测的多信使光变曲线、选定时间的喷射物结构快照，适合用作初始研究长期模拟的条件，以及可用于进一步核合成计算的示踪粒子轨迹。这些数据集还将与当前地面探测器（例如先进的 LIGO 和全球类似探测器）、未来天基引力波任务（例如 LISA）以及即将推出的主要天文观测站（例如维拉·鲁宾的大型综合巡天望远镜（LSST）。该奖项支持购买新的数据存储和分析集群，以支持多信使天体物理学。该系统将由 16 个分析节点和 3 PB 在线存储组成，托管在罗彻斯特理工学院 (RIT)。 拟议的系统将使 RIT 小组及其合作者能够存储和分析在本地计算集群和国家超级计算设施（例如 Frontera exascale 系统）中进行的模拟所产生的非常大的数据集。新系统将能够对致密天体进行高精度建模，这些致密天体是引力波的关键来源，例如黑洞和中子星双星以及超大质量黑洞双星。由于其复杂性和规模，这些模拟通常需要多年的模拟后分析才能提取相关的物理现象。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Search for continuous gravitational waves from 20 accreting millisecond x-ray pulsars in O3 LIGO data

在 O3 LIGO 数据中搜索 20 个吸积毫秒 X 射线脉冲星的连续引力波

• DOI: 10.1103/physrevd.105.022002
• 发表时间: 2022-01
• 期刊: Physical Review D
• 影响因子: 5
• 作者: Abbott, R.;Abbott, T. D.;Acernese, F.;Ackley, K.;Adams, C.;Adhikari, N.;Adhikari, R. X.;Adya, V. B.;Affeldt, C.;Agarwal, D.;et al
• 通讯作者: et al

Mass-ratio and Magnetic Flux Dependence of Modulated Accretion from Circumbinary Disks

环形盘调制吸积的质量比和磁通量依赖性

• DOI: 10.3847/1538-4357/ac2229
• 发表时间: 2021-11
• 期刊: The Astrophysical Journal
• 作者: Noble, Scott C.;Krolik, Julian H.;Campanelli, Manuela;Zlochower, Yosef;Mundim, Bruno C.;Nakano, Hiroyuki;Zilhão, Miguel
• 通讯作者: Zilhão, Miguel

Local and approximate classification of spacetimes in the transverse frames

横向框架中时空的局部和近似分类

• DOI: 10.1103/physrevd.104.044047
• 发表时间: 2021-08
• 期刊: Physical Review D
• 影响因子: 5
• 作者: Rosato, Nicole;Nakano, Hiroyuki;Lousto, Carlos O.
• 通讯作者: Lousto, Carlos O.

Eccentricity estimate for black hole mergers with numerical relativity simulations

通过数值相对论模拟估计黑洞合并的偏心率

• DOI: 10.1038/s41550-021-01568-w
• 发表时间: 2022-03
• 期刊: Nature Astronomy
• 影响因子: 14.1
• 作者: Gayathri, V.;Healy, J.;Lange, J.;O’Brien, B.;Szczepańczyk, M.;Bartos, Imre;Campanelli, M.;Klimenko, S.;Lousto, C. O.;O’Shaughnessy, R.
• 通讯作者: O’Shaughnessy, R.

HARM3D+NUC: A New Method for Simulating the Post-merger Phase of Binary Neutron Star Mergers with GRMHD, Tabulated EOS, and Neutrino Leakage

HARM3D NUC：利用 GRMHD、表格 EOS 和中微子泄漏模拟双中子星并合后阶段的新方法

• DOI: 10.3847/1538-4357/ac1119
• 发表时间: 2021-09
• 期刊: The Astrophysical Journal
• 作者: Murguia;Noble, Scott C.;Roberts, Luke F.;Ramirez;Werneck, Leonardo R.;Kolacki, Michael;Etienne, Zachariah B.;Avara, Mark;Campanelli, Manuela;Ciolfi, Riccardo;et al
• 通讯作者: et al