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

该奖项将支持对短时伽马射线暴(SGRb)及其前身本质的理解。SGRB是宇宙中能量最高的爆炸之一，由两颗中子星或中子星与黑洞的碰撞引发。它伴随着各种波长的电磁辐射的发射，以及引力波。虽然典型的双中子星碰撞GW170817以前所未有的方式扩展了我们对这种爆发的理解，但它也留下了一些悬而未决的问题。一个这样的问题是这些碰撞的命运，人们猜测这些碰撞会影响SGRBs的余辉。这项工作将支持对LIGO和处女座中引力波(GW)信号的SGRB对应物的多信使天文观测，以了解它们产生的残留物的性质。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探测器中的噪声伪迹，这些探测器可以伪装成来自这些碰撞的GW信号。它还将有助于在华盛顿州立大学三城校区培训学生，以及在邻近地区开展教育和公共宣传活动。这个校园靠近LIGO-Hanford网站，迎合了很大比例的西班牙裔和印第安人人口。该项目将在LIGO、处女座和KAGRA的第四次和第五次观测期间加强引力波观测的科学开发。它将减少这些探测器发出的低延迟警报中的错误警报数量。为此，它将专门针对涉及中子星的双星产生的GW信号。这有助于天文台跟踪这些GW警报，以探测伴随的电磁和粒子排放。该项目将通过以下方式实现这一目标：(A)有助于更全面地核算非高斯瞬时噪声源；(B)确定噪声源的不同类型的非线性耦合；(C)利用机器学习解决方案和自动数据质量输入，改进在线全球预报警报的重要性衡量。它还将开发基于信号的噪声鉴别器，或“卡方”测试，特别是那些对大多数错误警报负有责任的噪声伪迹。对这些双星的观测将被用来提高我们对中子星状态方程和引力波辅助测量哈勃常数的理解。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 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.