The multimessenger Universe as a training ground for frontier AI
作为前沿人工智能训练场的多信使宇宙
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.
展开 ▾创新点:将多信使天文学(MMA)的数据特性——物理定律约束和可模拟层次——定义为检验和训练前沿AI的独特“沙盒”,推动AI与天文学的深度协同。
多信使天文学正从稀疏事件迈进持续性数据洪流,传统方法日益成为发现瓶颈;同期AI也逼近人类生成数据的学习极限。本文基于2025年范德比尔特大学研讨会,指出MMA提供的物理驱动型数据能弥补纯数据驱动AI的可靠性缺陷,成为开发物理常识AI的试验平台。展望未来,跨学科合作将催生能实时融合引力波、中微子与电磁信号并自主识别新物理的AI系统,从而重塑天文学发现范式,并为国家级科研基础设施与产业应用绘制路线图。
接收 2026-06-03 · 刊出 2026-07-10 · 收录 2026-07-22