What is it about?
Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading to a computational cost that is too expensive in practice. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources. As a proof of concept, we show that a science-driven and uniform multi-stage self-attention-based deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method exhibits a detection rate exceeding 99% in identifying signals from various sources, with the signal-to-noise ratio at 50, at a false alarm rate of 1%. while obtaining at least 95% similarity compared with target signals. We further demonstrate the interpretability and strong generalization behavior for several extended scenarios.
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Why is it important?
The first direct detection of GWs coming from coalescing binary black holes (BBHs) was made by the LIGO/Virgo Collaboration1, which verifies Einstein’s General Relativity. As detectors become more sensitive, more and more GW events are discovered, enabling a new era of multi-messenger astronomy. A total of 93 events have been reported in the three observations2. GWs have become a new probe allowing cross-validation with a variety of fundamental physical theories
Perspectives
In this article, we develop a uniform deep learning-based model for space-based GW signal detection and extraction for the four main GW sources of LISA. Our model is based on a self-attention sequence-to-sequence architecture that performs well when dealing with time series data. We have integrated long-term and short-term feature extraction blocks in our model to catch the dependency of the GW signal in high-dimensional latent space. To our knowledge, this is the first study to achieve high-accuracy detection and high-precision extraction for all main potential GW signals from space-based detection. The model’s intermediate results can be interpreted as the encoded signal waveform, revealing a strong correlation between what needs to be learned and what has been learned by the neural network. In our test results analysis, we obtained average overlaps (see eq. (14)) of 95% of our test samples being >0.95. It takes <10−2 s to perform extraction and detection, which is a factor of roughly 105 improvement over traditional approaches that often require several hours. Finally, our method can also achieve considerable extraction effects for some signals generated by other models that are not in the training dataset, demonstrating the strong generalization ability of the model.
Zhixiang Ren
Peng Cheng Laboratory
Read the Original
This page is a summary of: Space-based gravitational wave signal detection and extraction with deep neural network, Communications Physics, August 2023, Springer Science + Business Media,
DOI: 10.1038/s42005-023-01334-6.
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