论文标题
在重力波数据分析中改进了深度学习技术
Improved deep learning techniques in gravitational-wave data analysis
论文作者
论文摘要
近年来，卷积神经网络（CNN）和其他深度学习模型逐渐被引入重力波（GW）数据处理领域。与传统的匹配过滤技术相比，CNN在GW信号检测任务方面具有显着优势。此外，匹配的过滤技术基于现有理论波形的模板库，这使得很难找到超出理论期望的GW信号。在本文中，基于GW检测二进制黑洞的任务，我们将深度学习的优化技术（例如批处理标准化和辍学）引入了CNN模型。进行了模型性能的详细研究。通过这项研究，我们建议在GW信号检测任务中使用CNN模型中的批归归量表和辍学技术。此外，我们研究了CNN模型在GW信号的不同参数范围内的概括能力。我们指出，CNN模型对GW波形的参数范围的变化是可靠的。这是深度学习模型的主要优点，而不是匹配过滤技术。
In recent years, convolutional neural network (CNN) and other deep learning models have been gradually introduced into the area of gravitational-wave (GW) data processing. Compared with the traditional matched-filtering techniques, CNN has significant advantages in efficiency in GW signal detection tasks. In addition, matched-filtering techniques are based on the template bank of the existing theoretical waveform, which makes it difficult to find GW signals beyond theoretical expectation. In this paper, based on the task of GW detection of binary black holes, we introduce the optimization techniques of deep learning, such as batch normalization and dropout, to CNN models. Detailed studies of model performance are carried out. Through this study, we recommend to use batch normalization and dropout techniques in CNN models in GW signal detection tasks. Furthermore, we investigate the generalization ability of CNN models on different parameter ranges of GW signals. We point out that CNN models are robust to the variation of the parameter range of the GW waveform. This is a major advantage of deep learning models over matched-filtering techniques.
