# Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

arXiv: https://arxiv.org/abs/2105.06479 · HTML: https://mlsanity.com/paper/2105.06479/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 121 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 27 of 27)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [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)
- [DLHub: Model and Data Serving for Science](https://mlsanity.com/paper/1811.11213/) (2018)
- [Statistically-informed deep learning for gravitational wave parameter estimation](https://mlsanity.com/paper/1903.01998/) (2019)
- [Image-based deep learning for classification of noise transients in gravitational wave detectors](https://mlsanity.com/paper/1803.09933/) (2018)
- [Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders](https://mlsanity.com/paper/1903.03105/) (2019)
- [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)
- [Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey](https://mlsanity.com/paper/1812.02183/) (2018)
- [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)
- [Deep Multi-view Models for Glitch Classification](https://mlsanity.com/paper/1705.00034/) (2017)
- [Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations](https://mlsanity.com/paper/1906.03233/) (2019)
- [DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data](https://mlsanity.com/paper/1805.02296/) (2018)
- [Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms](https://mlsanity.com/paper/1912.07618/) (2019)
- [Eccentric, nonspinning, inspiral, Gaussian-process merger approximant for the detection and characterization of eccentric binary black hole mergers](https://mlsanity.com/paper/1711.06276/) (2017)
- [Machine-learning non-stationary noise out of gravitational wave detectors](https://mlsanity.com/paper/1911.09083/) (2019)
- [Physics of eccentric binary black hole mergers: A numerical relativity perspective](https://mlsanity.com/paper/1901.07038/) (2019)

## Built on by (most cited first, 10 of 10)

- [Applications of physics informed neural operators](https://mlsanity.com/paper/2203.12634/) (2022)
- [A FAIR and AI-ready Higgs boson decay dataset](https://mlsanity.com/paper/2108.02214/) (2021)
- [FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy](https://mlsanity.com/paper/2207.00611/) (2022)
- [Training Strategies for Deep Learning Gravitational-Wave Searches](https://mlsanity.com/paper/2106.03741/) (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)
- [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)
- [Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers](https://mlsanity.com/paper/2306.15728/) (2023)
- [AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers](https://mlsanity.com/paper/2310.00052/) (2023)
- [Transfer Learning Adapts to Changing PSD in Gravitational Wave Data](https://mlsanity.com/paper/2410.11911/) (2024)

## Abstract (arXiv)

We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the Universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos and cosmic rays cover a wide range of wavelengths and time scales. Combining and processing these datasets that vary in volume, speed and dimensionality requires new modes of instrument coordination, funding and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets, have powered the rise of artificial intelligence. This digital revolution now powers a multi-billion dollar industry, with far-reaching implications in technology and society. In this chapter we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in Multi-Messenger Astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017, to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.