机器学习辅助引力波探测器实时噪声回归的演示
Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors
AI总结：
本文演示了DeepClean卷积神经网络在LIGO引力波探测器中实时回归噪声，有效去除60Hz电源噪声及其边带，增强信号信噪比，且不损害天体物理信号，支持低延迟应用。
摘要：
实时噪声回归算法对于最大化LIGO、Virgo和KAGRA引力波探测器的科学成果至关重要。这包括提高信号的可探测性、源定位和合并前可探测性，从而能够实现快速的多信使后续观测。在本文中，我们展示了DeepClean的有效性，这是一种卷积神经网络架构，利用见证传感器来估计并从引力波应变数据中减去非线性和非平稳噪声。我们的研究使用了来自第三次观测运行的LIGO数据，并注入了致密双星信号。作为演示，我们使用DeepClean来减去由于电源主频率及其与非仪器噪声源非线性耦合产生的边带在60 Hz处的噪声。我们对注入信号的参数估计研究表明，DeepClean不会对数据中的潜在天体物理信号造成任何损害，同时可以增强潜在信号的信噪比。我们展示了DeepClean可用于低延迟噪声回归，以约1-2秒的延迟产生清洁的输出数据。我们还讨论了在训练DeepClean用于低延迟应用时可能考虑的各种因素。
英文摘要：
Real-time noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the detectability, source localization and pre-merger detectability of signals thereby enabling rapid multi-messenger follow-up. In this paper, we demonstrate the effectiveness of \textit{DeepClean}, a convolutional neural network architecture that uses witness sensors to estimate and subtract non-linear and non-stationary noise from gravitational-wave strain data. Our study uses LIGO data from the third observing run with injected compact binary signals. As a demonstration, we use \textit{DeepClean} to subtract the noise at 60 Hz due to the power mains and their sidebands arising from non-linear coupling with other instrumental noise sources. Our parameter estimation study on the injected signals shows that \textit{DeepClean} does not do any harm to the underlying astrophysical signals in the data while it can enhances the signal-to-noise ratio of potential signals. We show that \textit{DeepClean} can be used for low-latency noise regression to produce cleaned output data at latencies $\sim 1-2$\, s. We also discuss various considerations that may be made while training \textit{DeepClean} for low latency applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
