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A Method Of Detecting Gravitational Wave Based On Time-frequency . . . 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
Binary neutron stars gravitational wave detection based on wavelet . . . 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
Gravitational Wave-Signal Recognition Model Based on Fourier Transform . . . We proposed the first method to transform the time-domain signal of a gravitational wave into a frequency-domain signal via a discrete Fourier transform This method helps extract the characteristics of the gravitational wave signal when using a CNN
Time-frequency analysis of gravitational wave data Here an alternative time-frequency approach to gravitational wave data analysis is proposed that uses discrete, orthogonal wavelet wave packets The time domain data is mapped onto a uniform grid of time-frequency pixels
Extraction of gravitational wave signals with optimized convolutional . . . Gabbard et al have demonstrated that convolutional neural networks can achieve the sensitivity of matched filtering in the recognization of the gravitational-wave signals with high efficiency [Phys Rev Lett 120, 141103 (2018)] In this work we show that their model can be optimized for better accuracy
Some Optimizations on Detecting Gravitational Wave Using Convolutional . . . nalysis and selection of frequency analysis methods in CNN-based GW detection scheme Because the gravitational wave bursts are short tran-sients of gravitational radiation and their time-frequency information is a typical characteristic for GW candi-dates, time-frequency analysis is a proposed procedure in some GW detection studies based on
AI in Gravitational Wave Analysis, an Overview - INSPIRE-HEP Key research problems include glitch classification, glitch can-cellation, gravitational wave denoising, binary black hole signal detection, gravitational wave bursts, and minor issues that contribute to the overall understanding of gravitational wave phenomena