用于探测极端质量比旋近的扩张卷积神经网络
Dilated convolutional neural network for detecting extreme-mass-ratio inspirals
AI总结：
针对极端质量比旋近探测难度高、现有方法存在局限的问题，提出基于扩张因果卷积神经网络的频域端到端模型DECODE，在低信噪比数据上实现了高探测准确率与极快推理速度，适用于天基引力波数据分析。
摘要：
极端质量比旋近（Extreme Mass Ratio Inspirals, EMRIs）的探测十分复杂，因其波形复杂、持续时间长且信噪比（signal-to-noise ratio, SNR）低，相比致密双星并合更难被识别。基于匹配滤波的技术以计算量大著称，而现有的深度学习方法主要处理时域数据，往往受数据时长和信噪比的限制。此外，现有多数研究忽略了时延干涉测量（time-delay interferometry, TDI），在探测器响应计算中采用长波近似，因此限制了其处理激光频率噪声的能力。本研究提出DECODE，这是一款专注于EMRI信号探测的端到端模型，通过频域序列建模实现。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训练，可高效处理时长1年、信噪比约为50的多通道TDI数据。我们在累积信噪比为50至120的1年期数据上评估模型，在1%的假阳性率下实现了96.3%的真阳性率，且推理时间保持在0.01秒以内。通过对三个示例EMRI信号的可视化以验证可解释性与泛化性，DECODE展现出在未来天基引力波数据分析中的强大潜力。
英文摘要：
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.
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