Advanced LIGO and Advanced Virgo ground-based interferometers are instruments capable to detect gravitational wave signals exploiting advanced laser interferometry techniques. The underlying data analysis task consists in identifying specific patterns in noisy timeseries, but it is made extremely complex by the incredibly small amplitude of the target signals. In this scenario, the development of effective gravitational wave detection algorithms is crucial. We propose a novel layered framework for real-time detection of gravitational waves inspired by speech processing techniques and, in the present implementation, based on a state-of-the-art machine learning approach involving a hybridization of genetic programming and neural networks. The key aspects of the newly proposed framework are: the well structured, layered approach, and the low computational complexity. The paper describes the basic concepts of the framework and the derivation of the first three layers. Even if the layers are based on models derived using a machine learning approach, the proposed layered structure has a universal nature. Compared to more complex approaches, such as convolutional neural networks, which comprise a parameter set of several tens of MB and were tested exclusively for fixed length data samples, our framework has lower accuracy (e.g., it identifies 45% of low signal-to-noise-ration gravitational wave signals, against 65% of the state-of-the-art, at a false alarm probability of $10^{-2}$), but has a much lower computational complexity and a higher degree of modularity. Furthermore, the exploitation of short-term features makes the results of the new framework virtually independent against time-position of gravitational wave signals, simplifying its future exploitation in real-time multi-layer pipelines for gravitational-wave detection with new generation interferometers.

翻译：先进激光干涉引力波天文台（Advanced LIGO）和先进室女座引力波探测器（Advanced Virgo）是基于地面激光干涉技术的仪器，能够利用先进的激光干涉测量技术探测引力波信号。其底层数据分析任务在于识别含噪时间序列中的特定模式，但由于目标信号振幅极小，该任务变得极为复杂。在此背景下，开发有效的引力波探测算法至关重要。我们提出了一种受语音处理技术启发的分层框架，用于实时探测引力波。在当前实现中，该框架基于一种融合了遗传规划与神经网络的先进机器学习方法。新框架的核心特点在于：结构良好的分层设计以及低计算复杂度。本文阐述了该框架的基本概念及前三层的推导过程。尽管各层基于机器学习方法导出的模型，但所提出的分层结构具有普适性。相较于卷积神经网络等复杂方法（其参数集可达数十兆字节，且仅针对固定长度数据样本进行测试），我们的框架准确率较低（例如，在虚警概率为$10^{-2}$时，对低信噪比引力波信号的识别率为45%，而先进方法的识别率为65%），但计算复杂度显著降低且模块化程度更高。此外，该框架利用短时特征，使其结果几乎不受引力波信号时间位置的影响，从而简化了其未来在新一代干涉仪实时多层引力波探测管线中的应用。