Coherence DeepClean：迈向引力波探测器数据的自主去噪
Coherence DeepClean: Toward autonomous denoising of gravitational-wave detector data
AI总结：
本研究提出名为 Coherence DeepClean（CDC）的综合方法与网络基础设施，利用机器学习串联运行相干性监测与 DeepClean 去噪，显著提升引力波探测器灵敏度与双中子星探测距离。
摘要：
专为引力波观测设计的地面激光干涉仪（如 Advanced LIGO、Advanced Virgo 和 KAGRA）中的技术与环境噪声，在仪器的应变幅度谱密度中表现为窄带（<1Hz）或宽带（数十甚至数百Hz）的谱线与特征。当这些噪声源无法被识别或移除时，若存在对该噪声源敏感的见证传感器，则可在软件中对引力波应变通道进行去噪，从而恢复受影响频带上的仪器灵敏度。由于监测干涉仪与环境的辅助通道数量庞大，且可能存在非线性耦合，这种噪声搜寻与移除过程极具挑战性。在本研究中，我们提出了一种综合分析方法及相应的网络基础设施，利用机器学习技术迅速识别并移除软件中的噪声。该方法基于早期工作（称为 DeepClean），利用机器学习方法进行噪声的线性和非线性回归。我们演示了如何在接近在线数据采集时以串联方式运行和优化该流程；它从相干性监测分析开始，首先筛选并优先处理见证通道，随后供 DeepClean 使用。DeepClean 输出的去噪应变结果使双中子星探测距离提升了1.4%，这可转化为4.3%的敏感体积增加。我们将此网络基础设施称为 Coherence DeepClean（CDC），它是迈向地面干涉仪噪声相减自主运行的重要一步。
英文摘要：
Technical and environmental noise in ground-based laser interferometers designed for gravitational-wave observations like Advanced LIGO, Advanced Virgo and KAGRA, can manifest as narrow (<1Hz) or broadband ($10'$s or even $100'$s of Hz) spectral lines and features in the instruments' strain amplitude spectral density. When the sources of this noise cannot be identified or removed, in cases where there are witness sensors sensitive to this noise source, denoising of the gravitational-wave strain channel can be performed in software, enabling recovery of instrument sensitivity over affected frequency bands. This noise hunting and removal process can be particularly challenging due to the wealth of auxiliary channels monitoring the interferometry and the environment and the non-linear couplings that may be present. In this work, we present a comprehensive analysis approach and corresponding cyberinfrastructure to promptly identify and remove noise in software using machine learning techniques. The approach builds on earlier work (referred to as DeepClean) in using machine learning methods for linear and non-linear regression of noise. We demonstrate how this procedure can be operated and optimized in a tandem fashion close to online data taking; it starts off with a coherence monitoring analysis that first singles out and prioritizes witness channels that can then be used by DeepClean. The resulting denoised strain by DeepClean reflects a 1.4\% improvement in the binary neutron star range, which can translate into a 4.3\% increase in the sensitive volume. This cyber infrastructure we refer to as Coherence DeepClean, or CDC, is a significant step toward autonomous operations of noise subtraction for ground-based interferometers.
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