Automated design of digital filters using convolutional neural networks for extracting ringdown gravitational waves

Abstract The observation of gravitational waves is expected to allow new tests of general relativity to be performed. As the gravitational wave signal is hidden by detector noise in observed data, a method to reduce noise is required to analyze the ringdown phase of gravitational wave signals. Recently, some noise reduction methods based on a neural network have been proposed; however, the results of these methods must be considered with caution because the output can contain spurious components. To overcome this limitation, in this study, we developed a neural network–based method to design optimal digital filters for extracting ringdown gravitational wave signals. In this method, no spurious components appear in the output because the digital filters reduce the noise. We conducted simulations with waveforms of gravitational waves from binary black hole coalescence and confirmed that the proposed method designs appropriate filters that reduce detector noise.