A Chinese research team has developed an AI model called SpecCLIP that can interpret stellar spectral data from different telescopes, helping bridge gaps between massive astronomical datasets. Stellar spectra reveal key information such as temperature, chemical composition and surface gravity, but data from projects like China’s LAMOST and Europe’s Gaia satellite vary in method, resolution and wavelength, making joint analysis difficult.
Researchers from the National Astronomical Observatories of the Chinese Academy of Sciences and the University of Chinese Academy of Sciences applied contrastive learning techniques similar to large language models, enabling SpecCLIP to translate different types of spectra into a shared framework. Published in The Astrophysical Journal, the model can predict stellar parameters, search for similar spectra and identify unusual celestial objects.
The tool is expected to advance “Galactic archaeology” by helping scientists find rare, ancient stars and better understand the Milky Way’s formation. It has also been used in missions searching for Earth-like planets, improving the identification of potentially habitable worlds.
Credit : CGTN
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