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 dilated convolutional neural network for detecting extreme-mass-ratio inspirals (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 one-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.

中文翻译：

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用于检测极端质量比吸气的扩张卷积神经网络

由于其波形复杂、持续时间长和信噪比 (SNR) 低，极端质量比螺旋 (EMRI) 的检测非常复杂，与紧凑的二元聚结相比，它们的识别更具挑战性。虽然基于匹配过滤的技术以其计算需求而闻名，但现有的基于深度学习的方法主要处理时域数据，并且通常受到数据持续时间和信噪比的限制。此外，大多数现有工作忽略了时间延迟干涉测量（TDI），并在探测器响应计算中应用长波长近似，从而限制了它们处理激光频率噪声的能力。在这项研究中，我们引入了用于检测极端质量比螺旋的扩张卷积神经网络（DECODE），这是一种端到端模型，专注于通过频域序列建模进行 EMRI 信号检测。 DECODE 以扩张因果卷积神经网络为中心，在考虑 TDI-1.5 检测器响应的合成数据上进行训练，可以有效处理一年的多通道 TDI 数据，SNR 约为 50。我们根据累积 SNR 的一年数据评估我们的模型范围从 50 到 120，真阳性率达到 96.3%，假阳性率为 1%，推理时间保持在 0.01 秒以内。通过对三个展示的 EMRI 信号进行可视化以实现可解释性和泛化，DECODE 在未来天基引力波数据分析中展现出强大的潜力。