利用卷积AutoEncoders检测引力波异常
Anomaly Detection in Gravitational Waves data using Convolutional AutoEncoders
摘要：
到目前为止，由于LIGO-Virgo合作组织与高级LIGO和高级Virgo干涉仪的合作，已经宣布了50个引力波（GW）探测。信号的检测由于数据的噪声主导性质而变得复杂。GW检测过程中的传统方法需要在匹配滤波搜索的背景下精确了解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，分析容易受到称为毛刺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，我们提出了一种替代的通用方法研究GW数据的基础上检测异常。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。在手稿中提出的异常大多是基于GW发射的合并的二元黑洞系统。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。
英文摘要：
As of this moment, fifty gravitational waves (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. Presented in the manuscript anomalies 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.
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