Galaxy Circus: A New Paradigm for Anomalous Galaxy Discovery with Artificial Intelligence and Citizen Science

Modern telescopes yield unprecedented volumes of observational data, creating a significant challenge for efficiently identifying anomalous astronomical objects or events that could extend the boundaries of human knowledge. Hence, we propose a novel research paradigm that integrates artificial intelligence with citizen science to address the challenge. Specifically, a Bayesian neural network with a pre-trained Astronomical Large Vision Model (ALVM) first pre-screens data for anomalous galaxy candidates, which are then forwarded to citizen scientists for initial identifications in the manner of a “Pair Up” game. At last, with assistance from an astronomical natural language model (ANLM) with retrieval augmented generation (RAG), experts examine the targets selected by citizen scientists and send the results of the analysis back to the citizen scientists for further learning. In our initial experimental application, named Galaxy Circus, 77 qualified citizen scientists have analysed 414391 galaxy images from the DESI Legacy Imaging Surveys with the help of ALVM in the "Pair Up" game, successfully identifying 1377 anomalous galaxies, including 387 (28.10%) merging galaxy candidates, 318 (23.09%) low-surface-brightness galaxy candidates, 3 (0.22%) gravitational lensing candidates, and 669 (48.58%) objects that defy conventional galaxy classification. Notably, 293 galaxies within the 669 galaxies have been previously documented in academic literature as abnormal galaxies. Through further vetting by 6 experts, our approach has identified 1084 new anomalous galaxies with over 9000 times faster than classical anomalous galaxy discovery paradigm (reducing the time required from approximately 75560 hours to just 8.8 hours), which requires scientists to vet each galaxy image, acceleration in scientific research, demonstrating that AI-citizen science integration provides a novel, efficient and reliable paradigm for identifying rare astronomical phenomena in the big data era. Galaxy circus: https://nadc.china-vo.org/galaxycircus/dist1-en