With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.

中文翻译：

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Gravity Spy：集成先进的 LIGO 探测器表征、机器学习和公民科学

随着引力波的首次直接探测，先进的激光干涉仪引力波天文台 (LIGO) 提供了一种感知宇宙的替代方法，开创了一个新的天文学领域。进行此类检测所需的极高灵敏度是通过将 LIGO 的所有敏感组件与非引力波干扰精确隔离来实现的。尽管如此，LIGO 仍然容易受到污染数据的各种仪器和环境噪声源的影响。特别值得关注的是称为毛刺的噪声特征，它们本质上是瞬态和非高斯的，并且以足够高的频率发生，因此两个 LIGO 探测器之间的偶然重合是不可忽略的。毛刺有多种时频幅度形态，随着检测器的发展出现新的形态。由于它们可以掩盖或模拟真实的引力波信号，因此对毛刺进行稳健的表征对于实现由 LIGO 的设计灵敏度预测的引力波检测率至关重要。由于数据量庞大，这对于 LIGO 科学合作组织的成员来说是一项艰巨的任务。在本文中，我们描述了一个创新项目，该项目将众包与机器学习相结合，以帮助完成对 LIGO 探测器记录的所有故障进行分类的挑战性任务。通过 Zooniverse 平台，我们从公众中招募和招募志愿者，将毛刺的时频表示图像分类为预先识别的形态类别，并发现随着检测器的发展而出现的新类别。此外，机器学习算法用于在对形态类的人类分类示例进行训练后对图像进行分类。利用两种分类方法的优势，我们创建了一种组合方法，旨在提高每个分类器的效率和准确性。由此产生的分类和表征应该有助于 LIGO 科学家确定毛刺的原因，然后从数据或探测器中完全消除它们，从而提高引力波观测的速率和准确性。我们使用来自 LIGO 首次观测运行的一小部分数据来演示这些方法。利用两种分类方法的优势，我们创建了一种组合方法，旨在提高每个分类器的效率和准确性。由此产生的分类和表征应该有助于 LIGO 科学家确定毛刺的原因，然后从数据或探测器中完全消除它们，从而提高引力波观测的速率和准确性。我们使用来自 LIGO 首次观测运行的一小部分数据来演示这些方法。利用两种分类方法的优势，我们创建了一种组合方法，旨在提高每个分类器的效率和准确性。由此产生的分类和表征应该有助于 LIGO 科学家确定毛刺的原因，然后从数据或探测器中完全消除它们，从而提高引力波观测的速率和准确性。我们使用来自 LIGO 首次观测运行的一小部分数据来演示这些方法。从而提高引力波观测的速率和精度。我们使用来自 LIGO 首次观测运行的一小部分数据来演示这些方法。从而提高引力波观测的速率和精度。我们使用来自 LIGO 首次观测运行的一小部分数据来演示这些方法。