  Extreme mass ratio inspirals (EMRIs) are among the most interesting
gravitational wave (GW) sources for space-borne GW detectors. However,
successful GW data analysis remains challenging due to many issues, ranging
from the difficulty of modeling accurate waveforms, to the impractically large
template bank required by the traditional matched filtering search method. In
this work, we introduce a proof-of-principle approach for EMRI detection based
on convolutional neural networks (CNNs). We demonstrate the performance with
simulated EMRI signals buried in Gaussian noise. We show that over a wide range
of physical parameters, the network is effective for EMRI systems with a
signal-to-noise ratio larger than 50, and the performance is most strongly
related to the signal-to-noise ratio. The method also shows good generalization
ability towards different waveform models. Our study reveals the potential
applicability of machine learning technology like CNNs towards more realistic
EMRI data analysis.
