Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
Authors: Yuduo Guo, Hao Zhang, Mingyu Li, Fujiang Yu, Yunjing Wu, Yuhan Hao, Song Huang, Yongming Liang, +5 more
Organizations: Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China · Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China · Department of Astronomy, Tsinghua University, Beijing, China · Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo, Kashiwa, Japan · National Astronomical Observatory of Japan, Tokyo, Japan · Institute for Cosmic Ray Research, The University of Tokyo, Kashiwa, Japan · College of AI, Tsinghua University, Beijing, China · School of Mathematics and Physics, Qinghai University, Xining, China · Tsinghua Wuxi Institute of Applied Technology, Wuxi, China
The detection limit of astronomical imaging observations is limited by several noise sources. Some of that noise is correlated between neighbouring image pixels and exposures, so in principle could be learned and corrected. We present an astronomical self-supervised transformer-based denoising algorithm (ASTERIS), that integrates spatiotemporal information across multiple exposures. Benchmarking on mock data indicates that ASTERIS improves detection limits by 1.0 magnitude at 90% completeness and purity, while preserving the point spread function and photometric accuracy. Observational validation using data from the James Webb Space Telescope (JWST) and Subaru telescope identifies previously undetectable features, including low-surface-brightness galaxy structures and gravitationally-lensed arcs. Applied to deep JWST images, ASTERIS identifies three times more redshift > 9 galaxy candidates, with rest-frame ultraviolet luminosity 1.0 magnitude fainter, than previous methods.
Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, ON M5S 3G4, Canada · National Research Council Herzberg Astronomy an Astrophysics Research Centre, 5071 West Saanich Road, Victoria, BC V9E 2E7, Canada · Department of Computer Science, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
School of Software Dalian University of Technology Dalian, China · National Astronomical Observatories Chinese Academy of Sciences Beijing, China · Research Institute of Highway Ministry of Transport Beijing, China