应用于引力波探测的深度学习网络结构复杂度研究

eep learning for gravitational wave detection has been a hot spot in recent years. The matched filtering method can be regarded as a neural network with a single convolutional layer, and the template is stored in the convolution kernel. By increasing the depth of the model, similar detection effects can be obtained while the parameter number is greatly reduced. We study the deep learning gravitational wave detection models withdifferent convolution kernel sizes, convolution kernel number, and convolution layer number. In addition, we investigate the detection effect of the model with batch normalization before the fully connected layer, and find that the detection accuracy of the model with a single convolutional layer increase from about 50% to more than 90% when the batch normalization is applied. A potential new method is provided for the compression of the number of matched filtering templates. By adding BN and full connection layer after matching filter, the number of matching templates may be greatly reduced. The generalization ability of the optimizedCNN models on different background noise is also investigated. We find that the model trained by the data with H1 background noise can be used to detect the data with L1 background noise, but the accuracy is slightly reduced.