欧洲太空总署与牛津大学等机构的研究人员利用人工智慧技术，成功在哈伯太空望远镜等历史观测档案中发现了数百个新的天文天体与不规则星系。同时，科学家开发出的虚拟研究助理能自动筛选超新星爆发的真实讯号，大幅减少了85%的人工检视时间，使天文学家能专注于更具创造性的研究。

伯明罕大学的研究人员利用AI模拟器快速建立恒星演化与太阳系外行星大气的复杂模型。以往需要数周甚至数月的模型模拟，在训练完成后仅需不到一毫秒即可完成。这项技术使科学家能高效连结光谱观测数据与理论物理过程，解开讯号背后成因的谜团。

面临智利鲁宾天文台等新一代望远镜带来的庞大数据洪流，天文学家正积极部署特化型的AI工具。且这些工具并不需要超级电脑或巨大的计算资源，仅凭个人电脑或单一GPU便能在几天内完成高效且永续的分析，从而改变未来的天文探索模式。

Researchers at institutions like the European Space Agency and Oxford University are leveraging artificial intelligence to discover hundreds of new astronomical (Key numbers: 85)

objects and irregular galaxies within archival data, such as Hubble Space Telescope images. Additionally, scientists have developed virtual research assistants that

At the University of Birmingham, researchers utilize AI emulators to rapidly construct complex models of stellar evolution and exoplanet atmospheres. These emulators reduce the simulation evaluation time from weeks or months to less than a millisecond once trained, enabling scientists to efficiently link spectral observations with specific physical processes and understand the underlying causes of the signals.