Getting the Best Multi-Messenger Science out of Gravitational-Wave Data with Better Modeling of its Noise and Automation

该奖项将支持对短持续时间伽马射线暴（SGRB）及其前身的性质的理解。SGRB是宇宙中能量最高的爆炸之一，由两颗中子星或一颗中子星星与黑洞的碰撞引发。它伴随着在很宽的波长范围内发射电磁辐射，以及引力波。虽然原型双星中子星星碰撞，GW170817，以前所未有的方式扩大了我们对这种爆发的理解，但它也留下了一些未解决的问题。其中一个问题是这些碰撞的命运，这些碰撞已经被证实会影响SGRB的余辉。这项工作将支持在LIGO和Virgo中对SGRB对应的引力波（GW）信号进行多信使天文观测，以了解它们产生的残余物的性质。该项目将通过开发机器学习工具，让公民科学家参与这项调查，以区分GW探测器中的噪声伪影，这些噪声伪影可以伪装成来自这些碰撞的GW信号。它还将为华盛顿州立大学三城校区的学生培训以及邻近地区的教育和公共宣传活动做出贡献。这个校园靠近LIGO汉福德网站，并迎合了西班牙裔和美国原住民人口的很大比例。该项目将在LIGO、Virgo和KAGRA的第四次和第五次观测运行期间加强对引力波观测的科学利用。它将减少这些检测器发出的低延迟警报中的假警报数量。为此，它将专门针对来自涉及中子星的双星的GW信号。这有助于天文观测站跟踪这些GW警报，以探测伴随的电磁和粒子发射。该项目将通过以下方式实现这一目标：（a）促进对非高斯瞬态噪声源的更完整核算，（B）识别噪声源的不同类型的非线性耦合，以及（c）通过利用机器学习解决方案和自动数据质量输入来改进在线GW警报的重要性测量。它还将开发基于信号的噪声鉴别器，或“卡方”测试，特别是针对那些造成大多数错误警报的噪声伪影。这些双星的观测将用于提高我们对中子星星状态方程和引力波辅助测量哈勃常数的理解。该奖项反映了NSF的法定使命，并被认为值得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来支持。

This award will support the pursuit for understanding the nature of short-duration gamma-ray bursts (SGRBs) and their progenitors. An SGRB is one of the most energetic explosions in the cosmos and is triggered by the collision of two neutron stars or a neutron star and a black hole. It is accompanied by the emission of electromagnetic radiation across a wide range of wavelengths, as well as gravitational waves. While the prototypical binary neutron star collision, GW170817, expanded our understanding of such bursts in unprecedented ways, it also left certain questions unresolved. One such question is the fate of these collisions, which have been conjectured to affect the afterglows of SGRBs. This work will support multi-messenger astronomical observations of SGRB counterparts of gravitational wave (GW) signals in LIGO and Virgo to understand the nature of the remnants produced by them. The project will involve citizen scientists in this investigation via the development of Machine Learning tools for distinguishing noise artifacts in GW detectors that can masquerade as GW signals from these collisions. It will also contribute to training students at the Tri-Cities campus of the Washington State University and education and public outreach activities in neighboring areas. This campus is close to the LIGO-Hanford site and caters to a large percentage of Hispanic and Native-American populations. This project will enhance the science exploitation of gravitational-wave observations with LIGO, Virgo, and KAGRA during their fourth and fifth observation runs. It will reduce the number of false alarms among the low-latency alerts issued by those detectors. For this purpose, it will specifically target GW signals from binaries involving neutron stars. This aids astronomical observatories that follow-up these GW alerts in their quest for detecting accompanying electromagnetic and particle emissions. The project will pursue this objective by (a) contributing to the more complete accounting of sources of non-Gaussian transient noise, (b) identifying different types of non-linear couplings of noise sources, and (c) improving the significance measurement of online GW alerts by utilizing Machine Learning solutions and automated data quality inputs. It will also develop signal-based noise discriminators, or “chi-square” tests, for targeting especially those noise artifacts that are responsible for a majority of false alerts. Observations of these binaries will be used to improve our understanding of the neutron star equation of state and gravitational-wave aided measurements of the Hubble constant.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.