Gravitational-Wave Astronomy and Astrophysics at Syracuse University

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）现在定期探测引力波的天体物理来源。科学现在有了一个天文台，可以让我们窥视爆炸恒星的核心，探测中子星的内部，探索碰撞黑洞的极端物理现象。双中子星合并GW170817的发现伴随着电磁波谱上的光，并开启了将引力作为多信使天文学工具的使用。LIGO和Virgo取得的巨大突破仅仅是我们探索引力波天空的开始。先进的LIGO与先进的处女座、KAGRA一起，最终将有下一代天文台，宇宙探索者和爱因斯坦望远镜。为了充分发挥日益增长的全球天文台网络的科学潜力，需要新的算法。该奖项将支持新的引力波搜索算法的开发，这将为下一代引力波搜索奠定基础，并为发现新型合并双星打开一扇窗。开发这些算法还将使学生掌握提高美国STEM劳动力竞争力所需的技能。本研究探讨了多探测器相干搜索算法，该算法利用了为引力波天文学开发的贝叶斯工具。探测引力波源的最佳方法是完全贝叶斯分析，它可以相干地组合来自多个探测器的引力波数据，并准确地模拟引力波信号和探测器噪声，其中可能包含非高斯瞬态噪声，即“故障”。目前使用的搜索使用启发式方法，这种方法近似于最优方法，并使它们能够在可用的计算资源上易于处理。虽然这是早期引力波天文学的有效策略，但这些方法牺牲了灵敏度和灵活性。算法将探索，使有效的层次分析引力波数据和快速贝叶斯证据估计。这种方法减少了搜索源和估计源参数之间的障碍，使得在故障、检测器非平稳性和重叠信号的建模中可以重用这两个领域的研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.