As of this moment, 50 gravitational wave (GW) detections have been announced, thanks to the observational efforts of the LIGO-Virgo collaboration, working with the Advanced LIGO and the Advanced Virgo interferometers. The detection of signals is complicated by the noise-dominated nature of the data. Conventional approaches in GW detection procedures require either precise knowledge of the GW waveform in the context of matched filtering searches or coincident analysis of data from multiple detectors. Furthermore, the analysis is prone to contamination by instrumental or environmental artifacts called glitches which either mimic astrophysical signals or reduce the overall quality of data. In this paper, we propose an alternative generic method of studying GW data based on detecting anomalies. The anomalies we study are transient signals, different from the slow non-stationary noise of the detector. The anomalies presented in the manuscript are mostly based on the GW emitted by the mergers of binary black hole systems. However, the presented study of anomalies is not limited only to GW alone, but also includes glitches occurring in the real LIGO/Virgo dataset available at the Gravitational Waves Open Science Center. To search for anomalies we employ deep learning algorithms, namely convolutional autoencoders, which are trained on both simulated and real detector data. We demonstrate the capabilities of our deep learning implementation in the reconstruction of injected signals. We study the influence of the GW strength, defined in terms of matched filter signal-to-noise ratio, on the detection of anomalies. Moreover, we present the application of our method for the localization in time of anomalies in the studied time-series data. We validate the results of anomaly searches on real data containing confirmed gravitational wave detections; we thus prove the generalization capabilities of our method, towards detecting GWs unknown to our deep learning models during training.

截至目前，由于 LIGO-Virgo 合作、Advanced LIGO 和 Advanced Virgo 干涉仪的观测努力，已经宣布了 50 次引力波 (GW) 探测。信号的检测由于数据以噪声为主的性质而变得复杂。GW 检测程序中的传统方法需要在匹配过滤搜索的上下文中精确了解 GW 波形，或者需要对来自多个检测器的数据进行一致分析。此外，分析容易受到被称为故障的仪器或环境伪影的污染，这些伪影要么模仿天体物理信号，要么降低数据的整体质量。在本文中，我们提出了一种基于异常检测的研究 GW 数据的替代通用方法。我们研究的异常是瞬态信号，不同于检测器的缓慢非平稳噪声。手稿中出现的异常主要基于双黑洞系统合并所发出的引力波。然而，所提出的异常研究不仅限于 GW，还包括引力波开放科学中心提供的真实 LIGO/Virgo 数据集中发生的故障。为了搜索异常，我们采用深度学习算法，即卷积自编码器，它们在模拟和真实检测器数据上进行训练。我们展示了我们的深度学习实现在注入信号重建方面的能力。我们研究了根据匹配滤波器信噪比定义的 GW 强度对异常检测的影响。而且，我们介绍了我们的方法在研究的时间序列数据中的异常时间定位的应用。我们验证了对包含已确认引力波探测的真实数据的异常搜索结果；因此，我们证明了我们的方法的泛化能力，用于在训练期间检测我们的深度学习模型未知的 GW。