mlsanityData to 14 Oct 2026

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

arXiv 2105.06479 ↗First posted 13 May 2021astro-ph.IM astro-ph.HE cs.AIcited by 10

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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.

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

27 of 121 references.

References · 27

  1. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  2. 2019Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomycited by 36
  3. 2017Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Datacited by 33
  4. 2020Complete parameter inference for GW150914 using deep learningcited by 25
  5. 2016Deep Neural Networks to Enable Real-time Multimessenger Astrophysicscited by 24
  6. 2020Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detectioncited by 20
  7. 2020Gravitational-wave parameter estimation with autoregressive neural network flowscited by 19
  8. 2019Learning Bayesian posteriors with neural networks for gravitational-wave inferencecited by 18
  9. 2019Enabling real-time multi-messenger astrophysics discoveries with deep learningcited by 18
  10. 2018DLHub: Model and Data Serving for Sciencecited by 17
  11. 2019Statistically-informed deep learning for gravitational wave parameter estimationcited by 14
  12. 2018Image-based deep learning for classification of noise transients in gravitational wave detectorscited by 10
15 more
  1. 2019Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoderscited by 10
  2. 2018Reduced-order modeling with artificial neurons for gravitational-wave inferencecited by 9
  3. 2020Detection of gravitational-wave signals from binary neutron star mergers using machine learningcited by 9
  4. 2020Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructurecited by 7
  5. 2020Detection of Gravitational Waves Using Bayesian Neural Networkscited by 7
  6. 2020Genetic-algorithm-optimized neural networks for gravitational wave classificationcited by 6
  7. 2018Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Surveycited by 5
  8. 2020Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergerscited by 5
  9. 2017Deep Multi-view Models for Glitch Classificationcited by 4
  10. 2019Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculationscited by 4
  11. 2018DIRECT: Deep Discriminative Embedding for Clustering of LIGO Datacited by 3
  12. 2019Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiogramscited by 3
  13. 2017Eccentric, nonspinning, inspiral, Gaussian-process merger approximant for the detection and characterization of eccentric binary black hole mergerscited by 2
  14. 2019Machine-learning non-stationary noise out of gravitational wave detectorscited by 2
  15. 2019Physics of eccentric binary black hole mergers: A numerical relativity perspectivecited by 1

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Cited by year

Citing papers per year: 2021: 4; 2022: 3; 2023: 2; 2024: 1.

10 papers.

  1. 2022Applications of physics informed neural operatorscited by 33
  2. 2021A FAIR and AI-ready Higgs boson decay datasetcited by 14
  3. 2022FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopycited by 10
  4. 2021Training Strategies for Deep Learning Gravitational-Wave Searchescited by 5
  5. 2021Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergerscited by 5
  6. 2022Inference-optimized AI and high performance computing for gravitational wave detection at scalecited by 5
  7. 2021AI 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 mergerscited by 3
  8. 2023Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergerscited by 1
  9. 2023AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergerscited by 0
  10. 2024Transfer Learning Adapts to Changing PSD in Gravitational Wave Datacited by 0