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A team of researchers in China has created an artificial intelligence model capable of interpreting stellar spectral data collected from various telescopes, showcasing the significant potential of this technology in astronomical research.
Scientists from the National Astronomical Observatories of the Chinese Academy of Sciences, the University of Chinese Academy of Sciences, and other institutions integrated concepts akin to large language models into the field of astronomy. They utilized a contrastive learning approach to develop the SpecCLIP model, which can independently learn and establish inherent connections between spectral data from different sources. This advancement was detailed in a publication in The Astrophysical Journal on February 11.
Stellar spectra hold vital information about stars, including temperature, chemical makeup, and surface gravity. Analyzing this data allows astronomers to trace the evolutionary history of the Milky Way from its origins.
The AI model can predict key stellar atmospheric parameters and elemental abundances, facilitate spectral similarity searches, and even assist in identifying unusual celestial objects. Its capabilities have already been employed in advanced exploration missions, such as searching for Earth-like planets.
One major challenge for researchers is that different survey projects—like China’s Large Sky Area Multi-Object Fiber Spectroscopic Telescope and Europe’s Gaia satellite—collect spectral data using varying methods, resolutions, and wavelength ranges. These datasets are akin to stories told in different dialects, making it difficult to combine them for large-scale analysis.
The SpecCLIP model acts as a translator, converting the low-resolution spectra from LAMOST and the high-precision data from Gaia into a common language. This enables scientists to perform integrated analyses effortlessly, aligning and transforming data across different instruments and surveys.
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