Gravitational-Wave Astronomy and Astrophysics at Syracuse University

雪城大学引力波天文学和天体物理学

• 批准号: 2309240
• 负责人: Alexander Nitz
• 金额: $ 18万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309240&HistoricalAwards=false
• 关键词: Gravitational; Wave; Astronomy; Astrophysics; Syracuse

Gravitational-wave astronomy has given humanity a completely new way to observe the universe. The National Science Foundation’s Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) now routinely detects astrophysical sources of gravitational waves. Science now has an observatory that will allow us to peer into the cores of exploding stars, probe the interiors of neutron stars, and explore the extreme physics of colliding black holes. The discovery of the binary neutron star merger GW170817 was accompanied by light across the electromagnetic spectrum and inaugurated the use of gravity as an instrument of multi-messenger astronomy. The dramatic breakthroughs made by LIGO and Virgo are only the beginning of our exploration of the gravitational-wave sky. Advanced LIGO is joined by Advanced Virgo, KAGRA, and eventually there will be the next-generation observatories, Cosmic Explorer and Einstein Telescope. New algorithms are required to achieve the full scientific potential of the growing global observatory network. This award will support the development of new gravitational-wave search algorithms that will lay the foundation for the next generation and open a window to the discovery of new classes of merging binaries. Developing these algorithms will also equip students with the skills needed to enhance the competitiveness of the U.S. STEM workforce.This research investigates multi-detector coherent search algorithms that take advantage of Bayesian tools developed for gravitational-wave astronomy. The optimal approach to detect gravitational-wave sources is a fully Bayesian analysis that can coherently combine gravitational-wave data from multiple detectors and accurately model both gravitational-wave signals and detector noise which may contain non-Gaussian transient noise, aka ’glitches’. Currently employed searches employ heuristics that approximate the optimal approach and enable them to be tractable on available computing resources. While this has been an effective strategy for the early era of gravitational-wave astronomy, these approaches sacrifice sensitivity and flexibility. Algorithms will be explored that enable efficient hierarchical analysis of gravitational-wave data and rapid Bayesian evidence estimation. This approach reduces the barriers between searching for sources and the estimation of source parameters, enabling the reuse of research in either regime into the modeling of glitches, detector nonstationarity, and overlapping signals.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.

引力波天文学为人类提供了一种观察宇宙的全新方式，美国国家科学基金会的先进激光干涉仪引力波天文台（LIGO）现在可以定期探测引力波的天体物理源。进入爆炸恒星的核心，探测中子星的内部，并探索碰撞黑洞的极端物理双中子星合并的发现。 GW170817 伴随着整个电磁波谱的光，开创了利用重力作为多信使天文学工具的先河。 LIGO 和 Virgo 所取得的巨大突破只是我们探索引力波天空的开始。由 Advanced Virgo、KAGRA 开发，最终将有下一代天文台、宇宙探索者和爱因斯坦望远镜，需要新的算法来实现全部科学潜力。该奖项将支持新的引力波搜索算法的开发，这将为下一代奠定基础，并为发现新类别的合并双星打开一扇窗户。开发这些算法也将为学生提供装备。具有增强美国 STEM 劳动力竞争力所需的技能。这项研究研究了多探测器相干搜索算法，该算法利用为引力波天文学开发的贝叶斯工具进行探测的最佳方法。引力波源是一种完全贝叶斯分析，可以连贯地组合来自多个探测器的引力波数据，并准确地对可能包含非高斯瞬态噪声（也称为“故障”）的引力波信号和探测器噪声进行建模。虽然这对于引力波天文学的早期时代来说是一种有效的策略，但这些方法牺牲了灵敏度和性能。将探索能够对引力波数据进行有效的分层分析和快速贝叶斯证据估计的算法，这种方法减少了搜索源和源参数估计之间的障碍，从而能够将任一领域的研究重新用于建模。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。