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

TL;DR

This paper introduces DECODE, a dilated convolutional neural network that efficiently detects EMRI signals in gravitational wave data, handling long-duration, low-SNR signals with high accuracy and interpretability.

Contribution

The study presents a novel end-to-end frequency domain model using dilated causal convolutions for EMRI detection, incorporating TDI response and synthetic data training.

Findings

Achieves 96.3% true positive rate at 1% false positive rate.

Processes one year's multichannel TDI data in less than 0.01 seconds.

Demonstrates robustness across SNRs from 50 to 120.

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.