Title: A Method Of Detecting Gravitational Wave Based On Time-frequency Analysis And Convolutional Neural Networks,
Abstract: This work investigated the detection of gravitational wave (GW) from
simulated damped sinusoid signals contaminated with Gaussian noise. We proposed
to treat it as a classification problem with one class bearing our special
attentions. Two successive steps of the proposed scheme are as following:
first, decompose the data using a wavelet packet and represent the GW signal
and noise using the derived decomposition coefficients; Second, detect the
existence of GW using a convolutional neural network (CNN). To reflect our
special attention on searching GW signals, the performance is evaluated using
not only the traditional classification accuracy (correct ratio), but also
receiver operating characteristic (ROC) curve, and experiments show excelllent
performances on both evaluation measures. The generalization of a proposed
searching scheme on GW model parameter and possible extensions to other data
analysis tasks are crucial for a machine learning based approach. On this
aspect, experiments shows that there is no significant difference between GW
model parameters on identification performances by our proposed scheme.
Therefore, the proposed scheme has excellent generalization and could be used
to search for non-trained and un-known GW signals or glitches in the future GW
astronomy era.