Some Optimizations on Detecting Gravitational Wave Using Convolutional Neural Network. (arXiv:1712.00356v2 [astro-ph.IM] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Li_X/0/1/0/all/0/1">Xiangru Li</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Yu_W/0/1/0/all/0/1">Woliang Yu</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Fan_X/0/1/0/all/0/1">Xilong Fan</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Babu_G/0/1/0/all/0/1">G. Jogesh Babu</a>
This work investigates the problem of detecting gravitational wave (GW)
events based on simulated damped sinusoid signals contaminated with white
Gaussian noise. It is treated as a classification problem with one class for
the interesting events. The proposed scheme consists of the following two
successive steps: decomposing the data using a wavelet packet, representing the
GW signal and noise using the derived decomposition coefficients; and
determining the existence of any GW event using a convolutional neural network
(CNN) with a logistic regression output layer. The characteristics of this work
is its comprehensive investigations on CNN structure, detection window width,
data resolution, wavelet packet decomposition and detection window overlap
scheme. Extensive simulation experiments show excellent performances for
reliable detection of signals with a range of GW model parameters and
signal-to-noise ratios. While we use a simple waveform model in this study, we
expect the method to be particularly valuable when the potential GW shapes are
too complex to be characterized with a template bank.
This work investigates the problem of detecting gravitational wave (GW)
events based on simulated damped sinusoid signals contaminated with white
Gaussian noise. It is treated as a classification problem with one class for
the interesting events. The proposed scheme consists of the following two
successive steps: decomposing the data using a wavelet packet, representing the
GW signal and noise using the derived decomposition coefficients; and
determining the existence of any GW event using a convolutional neural network
(CNN) with a logistic regression output layer. The characteristics of this work
is its comprehensive investigations on CNN structure, detection window width,
data resolution, wavelet packet decomposition and detection window overlap
scheme. Extensive simulation experiments show excellent performances for
reliable detection of signals with a range of GW model parameters and
signal-to-noise ratios. While we use a simple waveform model in this study, we
expect the method to be particularly valuable when the potential GW shapes are
too complex to be characterized with a template bank.
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