# Space-based gravitational wave signal detection and extraction with deep neural network

arXiv: https://arxiv.org/abs/2207.07414 · HTML: https://mlsanity.com/paper/2207.07414/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 50 references that are in the map.
- First posted on arXiv 15 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy](https://mlsanity.com/paper/1909.06296/) (2019)
- [Image-based deep learning for classification of noise transients in gravitational wave detectors](https://mlsanity.com/paper/1803.09933/) (2018)

## Built on by (most cited first, 2 of 2)

- [Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer](https://mlsanity.com/paper/2310.20172/) (2023)
- [Dilated convolutional neural network for detecting extreme-mass-ratio inspirals](https://mlsanity.com/paper/2308.16422/) (2023)

## Abstract (arXiv)

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.