# From One to Many: A Deep Learning Coincident Gravitational-Wave Search

arXiv: https://arxiv.org/abs/2108.10715 · HTML: https://mlsanity.com/paper/2108.10715/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 18 references that are in the map.
- First posted on arXiv 24 Aug 2021.

## Built on by (most cited first, 5 of 5)

- [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)

## Abstract (arXiv)

Gravitational waves from the coalescence of compact-binary sources are now routinely observed by Earth bound detectors. The most sensitive search algorithms convolve many different pre-calculated gravitational waveforms with the detector data and look for coincident matches between different detectors. Machine learning is being explored as an alternative approach to building a search algorithm that has the prospect to reduce computational costs and target more complex signals. In this work we construct a two-detector search for gravitational waves from binary black hole mergers using neural networks trained on non-spinning binary black hole data from a single detector. The network is applied to the data from both observatories independently and we check for events coincident in time between the two. This enables the efficient analysis of large quantities of background data by time-shifting the independent detector data. We find that while for a single detector the network retains 91.5% of the sensitivity matched filtering can achieve, this number drops to 83.9% for two observatories. To enable the network to check for signal consistency in the detectors, we then construct a set of simple networks that operate directly on data from both detectors. We find that none of these simple two-detector networks are capable of improving the sensitivity over applying networks individually to the data from the detectors and searching for time coincidences.