# Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy

arXiv: https://arxiv.org/abs/1909.06296 · HTML: https://mlsanity.com/paper/1909.06296/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2019.

## Built on by (most cited first, 30 of 36)

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- [Swift sky localization of gravitational waves using deep learning seeded importance sampling](https://mlsanity.com/paper/2111.00833/) (2021)
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- [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)
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- [Space-based gravitational wave signal detection and extraction with deep neural network](https://mlsanity.com/paper/2207.07414/) (2022)
- [Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware](https://mlsanity.com/paper/2407.19048/) (2024)
- [Hyperparameter optimization of hp-greedy reduced basis for gravitational wave surrogates](https://mlsanity.com/paper/2310.15143/) (2023)
- [Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer](https://mlsanity.com/paper/2310.20172/) (2023)
- [Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows](https://mlsanity.com/paper/2410.21076/) (2024)
- [Diffusion Model-based Parameter Estimation in Dynamic Power Systems](https://mlsanity.com/paper/2411.10431/) (2024)
- [Flexible Gravitational-Wave Parameter Estimation with Transformers](https://mlsanity.com/paper/2512.02968/) (2025)
- [Deep Learning Detection of Beyond-General-Relativity Deviations in Gravitational-Wave Signals: A Detection-Threshold Study with Real LIGO Noise](https://mlsanity.com/paper/2609.19416/) (2026)
- [Machine-Learning Love: classifying the equation of state of neutron stars with Transformers](https://mlsanity.com/paper/2210.08382/) (2022)
- [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)
- [Dilated convolutional neural network for detecting extreme-mass-ratio inspirals](https://mlsanity.com/paper/2308.16422/) (2023)

## Abstract (arXiv)

Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe O(100)s of transient GW events per year. The current methods used to estimate their source parameters employ optimally sensitive but computationally costly Bayesian inference approaches where typical analyses have taken between 6 hours and 5 days. For binary neutron star and neutron star black hole systems prompt counterpart electromagnetic (EM) signatures are expected on timescales of 1 second -- 1 minute and the current fastest method for alerting EM follow-up observers, can provide estimates in O(1) minute, on a limited range of key source parameters. Here we show that a conditional variational autoencoder pre-trained on binary black hole signals can return Bayesian posterior probability estimates. The training procedure need only be performed once for a given prior parameter space and the resulting trained machine can then generate samples describing the posterior distribution ~ 6 orders of magnitude faster than existing techniques.