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.

翻译：极端质量比旋近（EMRI）的探测因其复杂的波形、长持续时间和低信噪比（SNR）而具有复杂性，相比致密双星并合，其识别更具挑战性。尽管基于匹配滤波的技术以其计算需求著称，现有的深度学习方法主要处理时域数据，且通常受限于数据时长和信噪比。此外，现有工作大多忽略时延干涉测量（TDI）并在探测器响应计算中应用长波长近似，从而限制了其处理激光频率噪声的能力。在本研究中，我们提出DECODE，一种专注于频域序列建模的端到端EMRI信号检测模型。该模型以扩张因果卷积神经网络为核心，在考虑TDI-1.5探测器响应的合成数据上进行训练，能够高效处理一年时长、信噪比约为50的多通道TDI数据。我们在累积信噪比范围为50至120的一年期数据上评估该模型，在1%的误报率下达到了96.3%的真正率，推理时间保持在0.01秒以内。通过可视化三个展示的EMRI信号实现可解释性和泛化性，DECODE展现了在未来空间引力波数据分析中的强大潜力。