Abbott, B. P. et al. Multi-messenger observations of a binary neutron star merger. Astrophys. J. Lett. 848, L12 (2017).

Article 
ADS 

Google Scholar
 

Goldstein, A. et al. An ordinary short gamma-ray burst with extraordinary implications: Fermi-GBM detection of GRB 170817A. Astrophys. J. Lett. 848, L14 (2017).

Article 
ADS 

Google Scholar
 

Pooley, D. et al. GW170817 most likely made a black hole. Astrophys. J. Lett. 859, L23 (2018).

Article 
ADS 

Google Scholar
 

Ivezić, Ž et al. LSST: from science drivers to reference design and anticipated data products. Astrophys. J. 873, 111 (2019).

Article 
ADS 

Google Scholar
 

Aartsen, M. G. et al. The IceCube Neutrino Observatory: instrumentation and online systems. J. Instrum. 12, P03012 (2017).

Article 

Google Scholar
 

Baiotti, L. & Rezzolla, L. Binary neutron star mergers: a review of Einstein’s richest laboratory. Rep. Prog. Phys. 80, 096901 (2017).

Article 
ADS 
MathSciNet 

Google Scholar
 

Hao-Jui Kuan et al. Tidal resonance in binary neutron star inspirals. Phys. Rev. Lett. 135, 141403 (2025).

Article 
ADS 

Google Scholar
 

Metzger, B. D. Kilonovae. Living Rev. Relativ. 23, 1 (2019).

Article 
ADS 

Google Scholar
 

Ruiz, M. et al. Multimessenger binary mergers containing neutron stars: gravitational waves, jets, and γ-ray bursts. Front. Astron. Space Sci. 8, 686021 (2021).

Article 

Google Scholar
 

The LIGO Scientific Collaboration, the Virgo Collaboration & the KAGRA Collaboration. GWTC-4.0: updating the Gravitational-Wave Transient Catalog with observations from the first part of the fourth LIGO–Virgo–KAGRA observing run. Preprint at https://arxiv.org/abs/2508.18082 (2025).

Abbott, B. P. et al. GW170817: observation of gravitational waves from a binary neutron star inspiral. Phys. Rev. Lett. 119, 161101 (2017).

Article 
ADS 

Google Scholar
 

Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems Vol. 30 (eds Guyon, I. et al.) 5998–6008 (Curran Associates, 2017).

Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at https://arxiv.org/abs/2108.07258 (2021).

A New Golden Age of Discovery: Seizing the AI for Science Opportunity (Google DeepMind, 2024).

Karniadakis, G. E. et al. Physics-informed machine learning. Nat. Rev. Phys. 3, 422 (2021).

Article 

Google Scholar
 

Raissi, M., Perdikaris, P. & Karniadakis, G. E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems. J. Comput. Phys. 378, 686 (2019).

Article 
ADS 
MathSciNet 

Google Scholar
 

Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583 (2021).

Article 
ADS 

Google Scholar
 

Price, I. et al. Probabilistic weather forecasting with machine learning. Nature 637, 84–90 (2025).

Article 
ADS 

Google Scholar
 

Cranmer, K. et al. The frontier of simulation-based inference. Proc. Natl Acad. Sci. USA 117, 30055 (2020).

Article 
ADS 
MathSciNet 

Google Scholar
 

Reitze, D. et al. Cosmic Explorer: the U.S. contribution to gravitational-wave astronomy beyond LIGO. Bull. Am. Astron. Soc. 51, 035 (2019).


Google Scholar
 

Abac, A. et al. The science of the Einstein telescope. Preprint at https://arxiv.org/abs/2503.12263 (2025).

Amaro-Seoane, P. et al. Laser Interferometer Space Antenna. Preprint at https://arxiv.org/abs/1702.00786 (2017).

Jani, K. et al. Laser Interferometer Lunar Antenna (LILA): advancing the U.S. priorities in gravitational-wave and lunar science. Preprint at https://arxiv.org/abs/2508.11631 (2025).

Harms, J. et al. Lunar Gravitational-wave Antenna. Astrophys. J. 910, 1 (2021).

Article 
ADS 

Google Scholar
 

Aartsen, M. G. et al. IceCube-Gen2: the window to the extreme Universe. J. Phys. G 48, 060501 (2021).

Article 
ADS 

Google Scholar
 

Chen, H. et al. Cosmography with next-generation gravitational wave detectors. Class. Quantum Gravity 41, 125004 (2024).

Article 
ADS 

Google Scholar
 

Gupta, I. et al. Characterizing gravitational wave detector networks: from A♯ to cosmic explorer. Class. Quantum Gravity 41, 245001 (2024).

Article 
ADS 

Google Scholar
 

The Multimodal Universe Collaboration. The Multimodal Universe: enabling large-scale machine learning with 100TB of astronomical scientific data. Preprint at https://arxiv.org/abs/2412.02527 (2024).

Parker, L. et al. AION-1: a multimodal foundation model for astronomy. Preprint at https://arxiv.org/abs/2510.17960 (2025).

Liu, W. et al. StarEmbed: benchmarking time series foundation models on astronomical observations of variable stars. Preprint at https://arxiv.org/abs/2510.06200 (2025).

Chatterjee, C. et al. Pre-trained audio transformer as a foundational AI tool for gravitational waves. Preprint at https://arxiv.org/abs/2412.20789 (2024).

Chatterjee, C. et al. Machine learning confirms GW231123 is a ‘lite’ intermediate mass black hole merger. Astrophys. J. Lett. 995, L6 (2025).

Article 
ADS 

Google Scholar
 

Ma, Y. et al. A deep-learning search for technosignatures of 820 nearby stars. Nat. Astron. 7, 492 (2023).

ADS 

Google Scholar
 

Amsellem, A. et al. Data-driven trends and subpopulations in the gravitational wave binary black hole merger population with UMAP. Preprint at https://arxiv.org/abs/2603.06566 (2026).

Roulet, J. et al. labrador: a domain-optimized machine-learning tool for gravitational wave inference. Preprint at https://arxiv.org/abs/2604.08897 (2026).

Vallisneri, M. et al. nanograv/discovery: 0.5.1. Zenodo https://doi.org/10.5281/zenodo.17711453 (2025).

Dax, M. et al. Real-time inference for binary neutron star mergers using machine learning. Nature 639, 49–53 (2025).

Article 
ADS 

Google Scholar
 

Eliott, F. M., Stassun, K. & Kunda, M. IACI: a human-inspired computational architecture to help us understand visual data exploration. In Proc. 6th Annual Conference on Advances in Cognitive Systems 39–58 (Cognitive Systems Foundation, 2018).

Villaescusa-Navarro, F. et al. The Denario project: deep knowledge AI agents for scientific discovery. Preprint at https://arxiv.org/abs/2510.26887 (2025).

Laal, N. et al. Deep neural emulation of the supermassive black hole binary population. Astrophys. J. 982, 55 (2025).

Article 
ADS 

Google Scholar
 

Marx, E. et al. Aframe: a machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences. Phys. Rev. D 111, 042010 (2025).

Article 
ADS 

Google Scholar
 

Chatterjee, D. et al. AMPLFI: rapid likelihood-free inference of compact binary coalescences using accelerated hardware. Mach. Learn. Sci. Technol. 5, 045030 (2024).

Article 
ADS 

Google Scholar
 

Hu, E. J. et al. LoRA: low-rank adaptation of large language models. In Proc. 10th International Conference on Learning Representations (ICLR, 2021).

Rehemtulla, N. et al. Pre-training vision models for the classification of alerts from wide-field time-domain surveys. Preprint at https://arxiv.org/abs/2512.11957 (2026).

Udrescu, S.-M. & Tegmark, M. AI Feynman: a physics-inspired method for symbolic regression. Sci. Adv. 6, eaay2631 (2020).

Article 
ADS 

Google Scholar
 

Wadekar, D. et al. Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter. Proc. Natl Acad. Sci. USA 120, e2203856120 (2023).

Article 
MathSciNet 

Google Scholar
 

Templeton, A. et al. Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet (Anthropic, 2024).

Launching the Genesis Mission. White House Executive Order 14363 https://www.whitehouse.gov/presidential-actions/2025/11/launching-the-genesis-mission/ (24 November 2025).

Winning the AI Race: America’s AI Action Plan (White House Office of Science and Technology Policy, 2025).

Commission launches ‘Resource for AI Science in Europe’. European Commission Press Corner (3 November 2025).

Hiroshima Process International Guiding Principles for Organizations Developing Advanced AI Systems and Hiroshima Process International Code of Conduct for Organizations Developing Advanced AI Systems (Ministry of Foreign Affairs of Japan, 2023).

Hoos, H. & Irgens, M. Europe needs a CERN for artificial intelligence. Science Business (24 October 2023).

Rau, G. Space, AI technology, and the future of transatlantic security. Aspenia Online (2 August 2025).

Goldstein, A. Fermi Friday – August 17, 2018. NASA Goddard Space Flight Center https://fermi.gsfc.nasa.gov/fermi10/fridays/08172018.html (2018).