The article discusses the application of artificial intelligence technologies to enhance our understanding and perception of the universe. This represents a significant development in AI's capability to process and analyze complex astronomical and cosmological data.
Researchers developed a semi-supervised machine learning pipeline using vision transformers and k-Nearest Neighbor classifiers to automatically detect poor-quality exposures in astronomical imaging surveys. The method was successfully applied to the DECam Legacy Survey, identifying 780 problematic exposures that were verified through visual inspection.
Google's Gemini AI is being trained to identify exploding stars (supernovas) using few-shot learning techniques. This demonstrates AI's capability to recognize rare astronomical phenomena with minimal training examples.
