Yuduo Guo, Hao Zhang, Mingyu Li, Fujiang Yu, Yunjing Wu, Yuhan Hao, Song Huang, Yongming Liang, Xiaojing Lin, Xinyang Li, Jiamin Wu, Zheng Cai, Qionghai Dai
ASTERIS, a self-supervised denoising algorithm, enhances astronomical imaging detection limits by leveraging spatiotemporal data, improving detection by 1 magnitude and identifying previously undetectable features in deep space images.
Astronomers face challenges in detecting faint objects in space due to noise in their images. A new algorithm called ASTERIS uses advanced machine learning techniques to reduce this noise by analyzing patterns over multiple images and timeframes. This method helps astronomers see fainter objects than before, as demonstrated with data from the James Webb Space Telescope and Subaru telescope. ASTERIS has successfully identified faint galaxy structures and tripled the number of distant galaxies detected compared to older methods.
