# 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

arXiv: https://arxiv.org/abs/2112.07669 · HTML: https://mlsanity.com/paper/2112.07669/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 57 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 25 of 25)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://mlsanity.com/paper/1904.00962/) (2019)
- [Neural Spline Flows](https://mlsanity.com/paper/1906.04032/) (2019)
- [Real-time gravitational-wave science with neural posterior estimation](https://mlsanity.com/paper/2106.12594/) (2021)
- [Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy](https://mlsanity.com/paper/1909.06296/) (2019)
- [Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data](https://mlsanity.com/paper/1711.03121/) (2017)
- [Complete parameter inference for GW150914 using deep learning](https://mlsanity.com/paper/2008.03312/) (2020)
- [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)
- [Gravitational-wave parameter estimation with autoregressive neural network flows](https://mlsanity.com/paper/2002.07656/) (2020)
- [Learning Bayesian posteriors with neural networks for gravitational-wave inference](https://mlsanity.com/paper/1909.05966/) (2019)
- [Enabling real-time multi-messenger astrophysics discoveries with deep learning](https://mlsanity.com/paper/1911.11779/) (2019)
- [Group equivariant neural posterior estimation](https://mlsanity.com/paper/2111.13139/) (2021)
- [Statistically-informed deep learning for gravitational wave parameter estimation](https://mlsanity.com/paper/1903.01998/) (2019)
- [Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders](https://mlsanity.com/paper/1903.03105/) (2019)
- [Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources](https://mlsanity.com/paper/2105.06479/) (2021)
- [Reduced-order modeling with artificial neurons for gravitational-wave inference](https://mlsanity.com/paper/1811.05491/) (2018)
- [Detection of gravitational-wave signals from binary neutron star mergers using machine learning](https://mlsanity.com/paper/2006.01509/) (2020)
- [Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure](https://mlsanity.com/paper/2003.08394/) (2020)
- [Detection of Gravitational Waves Using Bayesian Neural Networks](https://mlsanity.com/paper/2007.04176/) (2020)
- [Genetic-algorithm-optimized neural networks for gravitational wave classification](https://mlsanity.com/paper/2010.04340/) (2020)
- [Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers](https://mlsanity.com/paper/2004.09524/) (2020)
- [Training Strategies for Deep Learning Gravitational-Wave Searches](https://mlsanity.com/paper/2106.03741/) (2021)
- [From One to Many: A Deep Learning Coincident Gravitational-Wave Search](https://mlsanity.com/paper/2108.10715/) (2021)
- [Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers](https://mlsanity.com/paper/2110.06968/) (2021)

## Built on by (most cited first, 3 of 3)

- [MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge](https://mlsanity.com/paper/2209.11146/) (2022)
- [FAIR AI Models in High Energy Physics](https://mlsanity.com/paper/2212.05081/) (2022)
- [Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars](https://mlsanity.com/paper/2406.17812/) (2024)

## Abstract (arXiv)

We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers. We trained AI models using 14 million waveforms, produced with the surrogate model NRHybSur3dq8, that include modes up to ℓ ≤ 4 and (5,5), except for (4,0) and (4,1), that describe binaries with mass-ratios q≤8, individual spins sz_1,2∈[-0.8, 0.8], and inclination angle θ∈[0,π].Our probabilistic AI surrogates can accurately constrain the mass-ratio, individual spins, effective spin, and inclination angle of numerical relativity waveforms that describe such signal manifold. We compared the predictions of our AI models with Gaussian process regression, random forest, k-nearest neighbors, and linear regression, and with traditional Bayesian inference methods through the PyCBC Inference toolkit, finding that AI outperforms all these approaches in terms of accuracy, and are between three to four orders of magnitude faster than traditional Bayesian inference methods. Our AI surrogates were trained within 3.4 hours using distributed training on 1,536 NVIDIA V100 GPUs in the Summit supercomputer.