SI2-SSE: Hearing the Signal through the Static: Realtime Noise Reduction in the Hunt for Binary Black Holes and other Gravitational Wave Transients

SI2-SSE：通过静电听到信号：寻找双黑洞和其他引力波瞬变过程中的实时降噪

• 批准号: 1642391
• 负责人: Chad Hanna
• 金额: $ 40万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-11-01 至 2020-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1642391&HistoricalAwards=false
• 关键词: SI2; SSE; Hearing; Signal; through

Gravitational waves - tiny ripples in the fabric of space - were detected for the first time in history on September 14, 2015 by the US-led gravitational wave observatory, LIGO. This watershed event has ushered in a new era of gravitational wave astronomy, which will transform our understanding of the Universe by providing information through a previously inaccessible channel. LIGO operates at the very edge of the sensitivity required to detect gravitational waves, and therefore, non-stationary noise caused by the environment, e.g., weather and man-made noise, limits LIGO's sensitivity to short-duration gravitational wave transient signals such as the one detected in September. In order to ensure the most opportunity for the advancement of science, this project aims to mine the extensive auxiliary information available in the LIGO observatories in realtime in order to mitigate the impact of non-stationary noise and increase the rate of transient gravitational wave detections. Doing so will afford increased opportunities for joint gravitational wave and electromagnetic observations, which are thought to be rare.This project aims to address a key piece of missing software infrastructure to use machine learning and inference techniques to utilize auxiliary information such as seismometers, microphones and various control loop signals, to identify non-stationary noise that couples to the gravitational-wave channel in near realtime. The software will be broken into three components: a signal decomposer, a signal classifier and a signal trainer. The signal classifier will determine, given the decomposed, instantaneous output of auxiliary channels and the training data, the probability that non-stationary noise is present. These software components will be built from reusable resources that can be applied across the time-domain gravitational wave community in data calibration, data transfer, and analysis.This project is supported by the Division of Advanced Cyberinfrastructure in the Directorate for Computer & Information Science & Engineering and the Physics Division in the Directorate of Mathematical and Physical Sciences.

引力波——空间结构中的微小涟漪——于 2015 年 9 月 14 日由美国领导的引力波观测站 LIGO 历史上首次检测到。这一分水岭事件开创了引力波天文学的新时代，它将通过以前无法访问的渠道提供信息，从而改变我们对宇宙的理解。 LIGO 的运行处于探测引力波所需灵敏度的边缘，因此，由环境引起的非平稳噪声（例如天气和人为噪声）限制了 LIGO 对短时引力波瞬态信号（例如 9 月份探测到的信号）的灵敏度。为了确保科学进步的最大机会，该项目旨在实时挖掘LIGO天文台中可用的广泛辅助信息，以减轻非平稳噪声的影响并提高瞬态引力波探测率。这样做将为引力波和电磁联合观测提供更多机会，而这被认为是罕见的。该项目旨在解决缺失的软件基础设施的关键部分，以使用机器学习和推理技术来利用地震计、麦克风和各种控制回路信号等辅助信息，以近乎实时地识别耦合到引力波通道的非平稳噪声。该软件将分为三个部分：信号分解器、信号分类器和信号训练器。给定辅助通道的分解瞬时输出和训练数据，信号分类器将确定非平稳噪声存在的概率。这些软件组件将由可重复使用的资源构建，可应用于整个时域引力波社区的数据校准、数据传输和分析。该项目得到了计算机与信息科学与工程局高级网络基础设施部门和数学与物理科学局物理部门的支持。

项目成果

iDQ: Statistical inference of non-gaussian noise with auxiliary degrees of freedom in gravitational-wave detectors

• DOI: 10.1088/2632-2153/abab5f
• 发表时间: 2020-05
• 期刊: Machine Learning: Science and Technology
• 作者: R. Essick;P. Godwin;C. Hanna;L. Blackburn;E. Katsavounidis