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

2015年9月14日，由美国领导的引力波天文台LIGO在历史上首次探测到引力波--空间结构中的微小涟漪。这一分水岭事件开启了引力波天文学的新时代，它将通过一个以前无法进入的渠道提供信息，从而改变我们对宇宙的理解。LIGO工作在探测引力波所需的灵敏度的边缘，因此，环境造成的非平稳噪声，如天气和人为噪声，限制了LIGO对短持续时间引力波瞬变信号的灵敏度，例如9月份检测到的信号。为了确保科学进步的最大机会，该项目旨在实时挖掘LIGO观测站可用的大量辅助信息，以减轻非平稳噪声的影响，提高瞬时引力波的探测率。这样做将提供更多的机会进行引力波和电磁联合观测，这被认为是罕见的。该项目旨在解决缺少的一个关键软件基础设施，即使用机器学习和推理技术来利用辅助信息，如地震仪、麦克风和各种控制回路信号，以接近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。在给定辅助信道的分解的瞬时输出和训练数据的情况下，信号分类器将确定存在非平稳噪声的概率。这些软件组件将由可重复使用的资源构建，这些资源可以跨时间域引力波社区应用于数据校准、数据传输和分析。该项目由计算机与信息科学与工程局的高级网络基础设施司和数学和物理科学局的物理司支持。

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.