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高级激光干涉仪引力波天文台(LIGO)现在例行探测引力波的天体物理源。科学现在有了一个天文台，可以让我们窥视正在爆炸的恒星的核心，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GW170817的发现伴随着电磁光谱上的光，并开创了利用重力作为多信使天文学工具的先河。LIGO和处女座取得的戏剧性突破只是我们探索引力波天空的开始。加入高级LIGO的还有高级处女座，KAGRA，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不断增长的全球天文台网络的全部科学潜力。该奖项将支持新引力波搜索算法的开发，这些算法将为下一代奠定基础，并为发现新的合并双星类别打开一扇窗。开发这些算法还将使学生掌握增强美国STEM工作队伍竞争力所需的技能。这项研究调查了利用为引力波天文学开发的贝叶斯工具的多探测器相干搜索算法。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，并准确地对引力波信号和探测器噪声进行建模，这些噪声可能包含非高斯瞬时噪声，也称为“毛刺”。目前使用的搜索使用接近最佳方法的启发式方法，并使它们能够在可用的计算资源上易于处理。虽然这是引力波天文学早期的一种有效策略，但这些方法牺牲了敏感性和灵活性。将探索能够有效地对引力波数据进行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信号源和估计信号源参数之间的障碍，使任何一种制度下的研究都能够重复用于毛刺、探测器非平稳性和重叠信号的建模。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.