# Detection of gravitational-wave signals from binary neutron star mergers using machine learning

arXiv: https://arxiv.org/abs/2006.01509 · HTML: https://mlsanity.com/paper/2006.01509/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 9 of 9)

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- [Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers](https://mlsanity.com/paper/2110.06968/) (2021)
- [Inference-optimized AI and high performance computing for gravitational wave detection at scale](https://mlsanity.com/paper/2201.11133/) (2022)
- [AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers](https://mlsanity.com/paper/2112.07669/) (2021)
- [Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data](https://mlsanity.com/paper/2306.11797/) (2023)
- [Learning and Interpreting Gravitational-Wave Features from CNNs with a Random Forest Approach](https://mlsanity.com/paper/2505.20357/) (2025)
- [AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivity](https://mlsanity.com/paper/2607.01372/) (2026)

## Abstract (arXiv)

As two neutron stars merge, they emit gravitational waves that can potentially be detected by earth bound detectors. Matched-filtering based algorithms have traditionally been used to extract quiet signals embedded in noise. We introduce a novel neural-network based machine learning algorithm that uses time series strain data from gravitational-wave detectors to detect signals from non-spinning binary neutron star mergers. For the Advanced LIGO design sensitivity, our network has an average sensitive distance of 130 Mpc at a false-alarm rate of 10 per month. Compared to other state-of-the-art machine learning algorithms, we find an improvement by a factor of 6 in sensitivity to signals with signal-to-noise ratio below 25. However, this approach is not yet competitive with traditional matched-filtering based methods. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert. We give an exact description of our testing procedure, which can not only be applied to machine learning based algorithms but all other search algorithms as well. We thereby improve the ability to compare machine learning and classical searches.