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The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due totheir complex waveforms, extended duration, and low signal-to-noise ratio(SNR), making them more challenging to be identified compared to compact binarycoalescences. While matched filtering-based techniques are known for theircomputational demands, existing deep learning-based methods primarily handletime-domain data and are often constrained by data duration and SNR. Inaddition, most existing work ignores time-delay interferometry (TDI) andapplies the long-wavelength approximation in detector response calculations,thus limiting their ability to handle laser frequency noise. In this study, weintroduce DECODE, an end-to-end model focusing on EMRI signal detection bysequence modeling in the frequency domain. Centered around a dilated causalconvolutional neural network, trained on synthetic data considering TDI-1.5detector response, DECODE can efficiently process a year's worth ofmultichannel TDI data with an SNR of around 50. We evaluate our model on 1-yeardata with accumulated SNR ranging from 50 to 120 and achieve a true positiverate of 96.3% at a false positive rate of 1%, keeping an inference time of lessthan 0.01 seconds. With the visualization of three showcased EMRI signals forinterpretability and generalization, DECODE exhibits strong potential forfuture space-based gravitational wave data analyses.

DECODE: DilatEd COnvolutional neural network for Detecting Extreme-mass-ratio inspirals