  This work investigates the detection of binary neutron stars gravitational
wave based on convolutional neural network (CNN). To promote the detection
performance and efficiency, we proposed a scheme based on wavelet packet (WP)
decomposition and CNN. The WP decomposition is a time-frequency method and can
enhance the discriminant features between gravitational wave signal and noise
before detection. The CNN conducts the gravitational wave detection by learning
a function mapping relation from the data under being processed to the space of
detection results. This function-mapping-relation style detection scheme can
detection efficiency significantly. In this work, instrument effects are
considered, and the noise are computed from a power spectral density (PSD)
equivalent to the Advanced LIGO design sensitivity. The quantitative
evaluations and comparisons with the state-of-art method matched filtering show
the excellent performances for BNS gravitational wave detection. On efficiency,
the current experiments show that this WP-CNN-based scheme is more than 960
times faster than the matched filtering.
