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

本研究引入了DECODE，一种以频域的序列建模为主的端到端模型，通过扩展因果卷积神经网络实现EMRI信号检测，能够高效处理一年的多通道TDI数据，并在信噪比在50到120之间的情况下，在0.01秒以内实现96.3%的真阳性率和1%的假阳性率。通过展示三个EMRI信号的可解释性和推广性，DECODE展示了未来基于空间的引力波数据分析的巨大潜力。

DECODE：检测极端质量比引力波的扩张卷积神经网络