Dilated convolutional neural network for detecting extreme-mass-ratio inspirals

• URL: http://arxiv.org/abs/2308.16422v3
• Date: Tue, 14 May 2024 13:40:05 GMT
• Title: Dilated convolutional neural network for detecting extreme-mass-ratio inspirals
• Authors: Tianyu Zhao, Yue Zhou, Ruijun Shi, Zhoujian Cao, Zhixiang Ren,
• Abstract summary: We introduce DECODE, an end-to-end model focusing on EMRI signal detection by sequence modeling in the frequency domain. 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%.
• Score: 8.809900732195281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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