标题： GWitchHunters：机器学习和公民科学以提高引力波探测器的性能 显示英文标题

标题： GWitchHunters: Machine Learning and citizen science to improve the performance of Gravitational Wave detector

摘要： 引力波为观察宇宙开辟了一个新的窗口，并为多信使观测宇宙源开启了新的时代。第二代地面探测器，如高级LIGO和高级Virgo，在探测黑洞和/或中子星合并产生的引力波信号方面非常成功。然而，为了达到所需的灵敏度，必须研究并去除背景噪声。特别是被称为“瞬态噪声事件”的“故障”可能会影响数据质量并模仿真实的天体物理信号，因此对它们进行表征并找到其来源至关重要，这项任务将支持Virgo和其他干涉仪的探测器表征活动。机器学习是实时表征和去除噪声故障最有前景的方法之一，从而提高干涉仪的灵敏度。这些机器学习算法训练数据集准备的一个关键输入可能来自公民科学倡议，其中志愿者参与对探测器收集的信号进行分类和分析。我们将介绍GWitchHunters，这是一个新的公民科学项目，专注于引力波噪声的研究，该项目是在REINFORCE项目（一个“与社会和为社会的科学”项目，由欧盟H2020计划资助）内开发的。我们将介绍该项目、其发展以及公民参与的关键任务，以及其对高级Virgo探测器噪声研究的影响。 显示英文摘要

摘要： The Gravitational waves have opened a new window on the Universe and paved the way to a new era of multimessenger observations of cosmic sources. Second-generation ground-based detectors such as Advanced LIGO and Advanced Virgo have been extremely successful in detecting gravitational wave signals from coalescence of black holes and/or neutron stars. However, in order to reach the required sensitivities, the background noise must be investigated and removed. In particular, transient noise events called "glitches" can affect data quality and mimic real astrophysical signals, and it is therefore of paramount importance to characterize them and find their origin, a task that will support the activities of detector characterization of Virgo and other interferometers. Machine learning is one of the most promising approaches to characterize and remove noise glitches in real time, thus improving the sensitivity of interferometers. A key input to the preparation of a training dataset for these machine learning algorithms can originate from citizen science initiatives, where volunteers contribute to classify and analyze signals collected by detectors. We will present GWitchHunters, a new citizen science project focused on the study of gravitational wave noise, that has been developed within the REINFORCE project (a "Science With And For Society" project funded under the EU's H2020 program). We will present the project, its development and the key tasks that citizens are participating in, as well as its impact on the study of noise in the Advanced Virgo detector.