论文标题
一种新型的多层模块化方法，用于重力波信号实时模糊识别
A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals
论文作者
论文摘要
先进的Ligo和先进的处女座地面干涉仪是能够检测利用高级激光干涉技术的引力波信号的仪器。潜在的数据分析任务包括识别嘈杂的时间表中的特定模式，但由于目标信号的极小振幅使它变得非常复杂。在这种情况下，有效重力波检测算法的发展至关重要。我们提出了一个新型的分层框架，用于实时检测受语音处理技术启发的引力波，以及在本实现中，基于一种最新的机器学习方法，涉及遗传编程和神经网络的杂交。新提出的框架的关键方面是：结构良好的分层方法和低计算复杂性。本文描述了框架的基本概念和前三层的推导。即使这些层是基于使用机器学习方法得出的模型，拟议的分层结构也具有普遍的性质。与更复杂的方法（例如卷积神经网络）相比，构成了几十个MB的参数集，并仅测试了固定长度数据样本的测试，我们的框架的准确性较低（例如，它在低较低的信号引力信号中识别出低较低的信号的45％，而$ 10^$ 10^$ 10^$ 10^$ 10.计算复杂性和更高程度的模块化。此外，对短期特征的开发使新框架的结果几乎独立于引力波信号的时位，从而简化了其在实时多层管道中的未来剥削，以通过新一代干涉仪进行重力波检测。
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.
