# Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders

arXiv: https://arxiv.org/abs/2107.12698 · HTML: https://mlsanity.com/paper/2107.12698/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 59 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Variational Autoencoders for New Physics Mining at the Large Hadron Collider](https://mlsanity.com/paper/1811.10276/) (2018)
- [Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data](https://mlsanity.com/paper/1711.03121/) (2017)
- [Deep Neural Networks to Enable Real-time Multimessenger Astrophysics](https://mlsanity.com/paper/1701.00008/) (2016)
- [Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection](https://mlsanity.com/paper/2012.08545/) (2020)
- [Accelerating Recurrent Neural Networks for Gravitational Wave Experiments](https://mlsanity.com/paper/2106.14089/) (2021)
- [How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series](https://mlsanity.com/paper/2004.11468/) (2020)
- [Improving significance of binary black hole mergers in Advanced LIGO data using deep learning : Confirmation of GW151216](https://mlsanity.com/paper/2010.08584/) (2020)

## Built on by (most cited first, 7 of 7)

- [Applications and Techniques for Fast Machine Learning in Science](https://mlsanity.com/paper/2110.13041/) (2021)
- [MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge](https://mlsanity.com/paper/2209.11146/) (2022)
- [A review of unsupervised learning in astronomy](https://mlsanity.com/paper/2406.17316/) (2024)
- [AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing](https://mlsanity.com/paper/2510.21935/) (2025)
- [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)
- [Reducci'on de ruido por medio de autoencoders: caso de estudio con la se~nal GW150914](https://mlsanity.com/paper/2510.00873/) (2025)
- [Parameter Estimation of Ringdown Quasinormal Modes with Autoencoder](https://mlsanity.com/paper/2609.14277/) (2026)

## Abstract (arXiv)

We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, i.e., without targeting a specific kind of source. We develop a custom architecture to analyze the data from two interferometers. We compare the obtained performance to that obtained with other autoencoder architectures and with a convolutional classifier. The unsupervised nature of the proposed strategy comes with a cost in terms of accuracy, when compared to more traditional supervised techniques. On the other hand, there is a qualitative gain in generalizing the experimental sensitivity beyond the ensemble of pre-computed signal templates. The recurrent autoencoder outperforms other autoencoders based on different architectures. The class of recurrent autoencoders presented in this paper could complement the search strategy employed for gravitational wave detection and extend the reach of the ongoing detection campaigns.