RUI: Supporting LIGO Calibration, Detector Characterization, and Data Analysis in O4

地基引力波探测器LIGO、Virgo和KAGRA的第四次观测运行（O 4）计划于2023年春季开始。O 4有望带来最大的地基引力波探测器网络和迄今为止最好的探测器灵敏度。该奖项支持凯尼恩学院LIGO科学合作（LSC）组，由PI L。韦德和合作PI M。韦德和四到六个本科研究生，在他们的研究追求在引力波物理学领域。凯尼恩学院LSC小组将为LIGO校准管道的运行和新型LIGO校准监视器的实施做出贡献，构建新的机器学习算法，用于使用探测器辅助通道的信息预测引力波应变数据中噪声伪影的存在，以及开发更强大和复杂的测量中子星状态方程的方法。 该奖项还支持两个已建立的，有影响力的天文学和物理学研究小组，从事高中和本科生的不同人群：凯尼恩学院无线电和光学天文学研究（ROAR）组和芒特弗农高中（MVHS）天文俱乐部。 ROAR是一个无障碍进入研究小组，目标是在凯尼恩学院的第一年和第二年潜在的物理专业招聘。 注重成果的年度报告的学生参与讨论当前的天文学和引力物理学研究课题，对感兴趣的课题进行独立调查，参与动手天文学和物理学相关活动，并为物理学和天文学的外联工作作出贡献。 MVHS天文学俱乐部在凯尼恩学院附近的当地高中运营，定期让10-20名高中生参与天文学和引力物理学的实践活动和研究课题。 该奖项有三个主要研究重点：校准，数据质量和中子星状态方程（EOS）参数估计。 该奖项将有助于LIGO校准管道的运行，用于产生最终校准的应变数据，这是由co-PI M领导的一项工作。Wade，开发和实施低延迟校准数据流的监控工具，并探索产生低延迟校准系统误差估计的方法。该奖项的第二个重点领域是从中子星星引力波事件推断中子星星状态方程。派湖Wade建议提高用于测量中子星星EOS的参数化模型的灵活性，开发推理软件以边缘化在其他情况下测量的参数，并构建软件以允许数据通知EOS模型中使用的参数数量。此外，PI L。韦德将有助于EOS推断在O 4中发现的任何双中子星星信号的努力。最后一组研究活动涉及探索对低延迟数据质量识别系统（iDQ）的改进。iDQ使用机器学习算法（MLAs）来预测应变数据中是否存在巨大的瞬态噪声事件（称为毛刺），仅使用辅助信息。派湖韦德和合作PI M。Wade建议建立分层MLA，以解释不同辅助子系统的可变性和毛刺形态的可变性，旨在提高在低延迟数据中识别毛刺的效率，从而提高低延迟天体物理分析可用的数据质量信息。最后的研究重点领域将是寻找一个新的理论信号，称为引力闪烁，这是由扰动沿着视线从一个紧凑的二元事件的观察者。 如果在存档的LIGO/Virgo数据中没有发现这样的信号，那么宇宙中扰动的密度就可以设定上限。该奖项反映了NSF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。

The fourth observing run (O4) for ground-based gravitational-wave detectors LIGO, Virgo, and KAGRA is scheduled to begin in the spring of 2023. O4 promises to bring the largest network of ground-based gravitational-wave detectors and the best detector sensitivity seen to date. This award supports the Kenyon College LIGO Scientific Collaboration (LSC) group, consisting of PI L. Wade and co-PI M. Wade, and between four and six undergraduate research students, in their research pursuits in the field of gravitational-wave physics. The Kenyon College LSC group will contribute to the operation of the LIGO calibration pipeline and implementation of a novel LIGO calibration monitor, the construction of new machine learning algorithms for predicting the presence of noise artifacts in gravitational-wave strain data using information from detector auxiliary channels, and the development of more robust and sophisticated ways of measuring the neutron-star equation of state. This award also supports two established, impactful astronomy and physics research groups that engage a diverse population of high school and undergraduate students: The Kenyon College Radio and Optical Astronomy Research (ROAR) group and the Mount Vernon High School (MVHS) astronomy club. ROAR is a no-barrier to entry research group that targets recruitment at first- and second-year potential physics majors at Kenyon College. Students in ROAR engage in discussions about current astronomy and gravitational physics research topics, do independent investigations into topics of interest, engage in hands-on astronomy and physics related activities, and contribute to outreach efforts in physics and astronomy. The MVHS astronomy club is run out of the local high school near Kenyon College and regularly engages 10-20 high school students in hands-on activities and research topics in astronomy and gravitational physics. This award has three main research focusses: calibration, data quality, and neutron-star equation of state (EOS) parameter estimation. This award will contribute to the operation of the LIGO calibration pipeline used to produce the final calibrated strain data, which is an effort lead by co-PI M. Wade, development and implementation of a monitoring tool for the low-latency calibrated data stream, and exploration of methods for producing a low-latency calibration systematic error estimate. The second focus area in this award is related to inferring the neutron star EOS from binary neutron star gravitational-wave events. PI L. Wade proposes to improve the flexibility of the parameterized models used to measure the neutron star EOS, develop inference software to marginalize over parameters measured in other contexts, and build software to allow the data to inform the number of parameters to use in the EOS models. Additionally, PI L. Wade will contribute to EOS inference efforts on any binary neutron star signals found in O4. The final set of research activities involve exploring improvements to the low-latency data quality identification system known as iDQ. iDQ uses machine learning algorithms (MLAs) to predict the presence of loud transient noise events, known as glitches, in the strain data using only auxiliary information. PI L. Wade and co-PI M. Wade propose to build hierarchical MLAs that will account for the variability in different auxiliary subsystems and the variability in glitch morphologies, aiming to improve the efficiency with which glitches can be identified in low-latency data, thereby improving the data quality information available to low-latency astrophysical analyses. The final research focus area will be in searching for a newly theorized signal known as a gravitational glint which is produced by perturbers along the line-of-sight from a compact binary event to an observer. If no such signals are found in archival LIGO/Virgo data then upper bounds could be set on the density of perturbers in the universe.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.