RUI: Expanding our View of the Gravitational-Wave Sky with Machine Learning and Numerical Relativity

该奖项支持相对论和相对论天体物理学的研究，并阐述了美国国家科学基金会“宇宙之窗”宏伟构想的优先领域。在爱因斯坦预言引力波存在一个世纪后，两个激光干涉仪引力波天文台(LIGO)探测器发现了穿过地球的引力波，打开了一扇新的宇宙之窗。引力波来自灾难性的遥远事件：碰撞的黑洞和中子星。LIGO需要惊人的精度才能清楚地观察到这些微弱的波，这需要精密隔离的探测器。捕捉黑洞和中子星碰撞的物理过程需要精确的波形模型。该奖项将在克里斯托弗·纽波特大学(CNU)建立一个新的研究小组，通过表征LIGO探测器以更好地了解探测器中有问题的噪声的来源，并通过改进用于解释观测信号的天体物理的波形模型，来应对这两个挑战。通过这些项目，国家天文台小组将在提高LIGO探测器数据的质量和提高与天文学界和公众分享的参数估计信息的准确性方面发挥关键作用。该奖项支持的学生将接受计算机编程、数据分析和机器学习方面的培训。这些重要的可转让技能将为学生在学术界和工业界广泛成功和有意义的STEM职业生涯做好准备。LIGO对引力波的敏感性受到非平稳噪声的限制，随着时间的推移，这种噪声会随着各种环境的影响而波动。这项工作将扩展PI与合作者开发的方法，使用套索线性回归将这些灵敏度变化与辅助仪器传感器关联起来，以确定最可能的原因。这种方法已经对识别随时间变化的噪声源很有用，但这项工作将针对有问题的持续噪声瞬变，它阻碍了引力波的搜索，并在几天和几周的过程中具有不同的速率。PI和学生还将对引力波候选事件的数据质量验证做出贡献，以确保更广泛的天文学社区能够获得必要的数据质量信息。未来几年，LIGO的预期改进将使人们能够观测到更多的黑洞，毫无疑问，其中一些具有有趣的不同性质，一些可能具有更高的信噪比。准确提取这些信号的天体物理参数需要与跨越潜在发现空间的模板波形进行比较。PI和她的学生将与模拟极端时空合作的成员一起研究代理建模(一种在昂贵但准确的数值相对论波形之间进行有效内插的方法)。这项工作将增强我们解释黑洞观测的能力，特别是那些具有大自旋的观测。这一奖项反映了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。

This award supports research in relativity and relativistic astrophysics and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. A century after Einstein predicted the existence of gravitational waves, the two Laser Interferometer Gravitational-wave Observatory (LIGO) detectors opened a new window on the universe by discovering gravitational waves passing through Earth, emanating from cataclysmic, distant events: colliding black holes and neutron stars. The phenomenal precision that LIGO needs to clearly observe these faint waves requires exquisitely isolated detectors. Capturing the physics of colliding black holes and neutron stars requires accurate waveform models. This award will establish a new research group at Christopher Newport University (CNU) to address both of these challenges, through characterizing LIGO detectors to better understand the origins of problematic noise in the detectors, and by improving the waveform models used to interpret the astrophysics of observed signals. Through these projects, the CNU group will play a crucial role in improving the quality of the LIGO detector data and the accuracy of the parameter estimation information that is shared with the astronomical community and the public. The students supported by this award will be trained in computer programming, data analysis, and machine learning. These important transferable skills will prepare the students for a wide range of successful and meaningful STEM careers in academia and industry. LIGO’s sensitivity to gravitational waves is limited by non-stationary noise, which fluctuates over time depending on various environmental influences. This work will extend a method developed by the PI with collaborators to correlate these variations in sensitivity with auxiliary instrumental sensors to determine the most possible causes, using lasso linear regression. This method has already been useful for identifying noise sources that change over the course of hours, but this work will target problematic persistent noise transients, which impede gravitational wave searches and have rates varying over the course of days and weeks. The PI and students will also contribute to data quality validation of gravitational wave candidate events, to ensure that the broader astronomical community has access to necessary data quality information. The expected improvements to LIGO in the coming years will enable the observation of many more black holes, doubtless some with interestingly different properties and some potentially having much higher signal-to-noise ratios. Accurately extracting the astrophysical parameters of these signals requires comparing to template waveforms that span the potential discovery space. The PI and her students will work on surrogate modeling (a way to efficiently interpolate between expensive but accurate numerical relativity waveforms), working with members of the Simulating eXtreme Spacetimes collaboration. This work will enhance our ability to interpret black hole observations, especially those with large spins.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.