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<p>Artificial intelligence (AI) is approaching the horizon of what can be learned from human-generated data just as multimessenger astronomy (MMA) enters a data-surge era in which conventional approaches are becoming a bottleneck in discovery. The convergence between MMA and AI is poised to transform both domains. Over the coming decade, MMA will turn rare cosmic events into continuous, multi-petabyte data streams that collectively sample physics across all four fundamental interactions. Unlike typical AI datasets, this deluge is governed by known physical laws and offers a unique hierarchy of simulability. MMA therefore provides a controlled environment where AI systems must distinguish instrumental noise, simulation approximation and genuine physical novelty. Drawing on discussions from the 2025 workshop ‘Multimessenger Astronomy in the Era of Foundational AI’ at Vanderbilt University, we argue that MMA can serve as both a proving ground for trustworthy, physics-informed AI and a scientific domain where AI itself will become indispensable for future discoveries. We outline the transformative science that this convergence can unlock and a roadmap for collaboration across astronomy, AI, industry and national research infrastructure.</p>

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The multimessenger Universe as a training ground for frontier AI

  • 期刊论文

  • 发表日期：

    2026年7月10日

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  • Abigail Petulante,
  • Chayan Chatterjee,
  • Karan Jani,
  • Jesse Spencer-Smith,
  • Stephen R. Taylor,
  • Adam A. Miller,
  • Keivan G. Stassun,
  • Gioia Rau,
  • Simon Peter Worden,
  • Jeff Shen,
  • Digvijay Wadekar,
  • Javier Roulet,
  • Deep Chatterjee,
  • Nima Laal,
  • Ignacio Magana Hernandez,
  • Daniel Moyer,
  • Matthew Johnson-Roberson,
  • Michele Vallisneri,
  • Suyash Deshmukh,
  • Ryan Nowicki

摘要

Artificial intelligence (AI) is approaching the horizon of what can be learned from human-generated data just as multimessenger astronomy (MMA) enters a data-surge era in which conventional approaches are becoming a bottleneck in discovery. The convergence between MMA and AI is poised to transform both domains. Over the coming decade, MMA will turn rare cosmic events into continuous, multi-petabyte data streams that collectively sample physics across all four fundamental interactions. Unlike typical AI datasets, this deluge is governed by known physical laws and offers a unique hierarchy of simulability. MMA therefore provides a controlled environment where AI systems must distinguish instrumental noise, simulation approximation and genuine physical novelty. Drawing on discussions from the 2025 workshop ‘Multimessenger Astronomy in the Era of Foundational AI’ at Vanderbilt University, we argue that MMA can serve as both a proving ground for trustworthy, physics-informed AI and a scientific domain where AI itself will become indispensable for future discoveries. We outline the transformative science that this convergence can unlock and a roadmap for collaboration across astronomy, AI, industry and national research infrastructure.