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Extreme mass ratio inspirals (EMRIs) are among the most interestinggravitational wave (GW) sources for space-borne GW detectors. However,successful GW data analysis remains challenging due to many issues, rangingfrom the difficulty of modeling accurate waveforms, to the impractically largetemplate bank required by the traditional matched filtering search method. Inthis work, we introduce a proof-of-principle approach for EMRI detection basedon convolutional neural networks (CNNs). We demonstrate the performance withsimulated EMRI signals buried in Gaussian noise. We show that over a wide rangeof physical parameters, the network is effective for EMRI systems with asignal-to-noise ratio larger than 50, and the performance is most stronglyrelated to the signal-to-noise ratio. The method also shows good generalizationability towards different waveform models. Our study reveals the potentialapplicability of machine learning technology like CNNs towards more realisticEMRI data analysis.

Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks