Adaptive Detection of Fast-moving Celestial Objects Using a Mixture-of-experts and Physical-inspired Neural Network

Abstract Fast-moving celestial objects are characterized by velocities across the celestial sphere that significantly differ from the motions of background stars. In observational images, these objects exhibit distinct shapes, contrasting with the typical appearances of stars. Depending on the observational method employed, these celestial entities may be designated as near-Earth objects or asteroids. Historically, fast-moving celestial objects have been observed using ground-based telescopes, where the relative stability of stars and Earth facilitated effective image differencing techniques alongside traditional fast-moving celestial object detection and classification algorithms. However, the growing prevalence of space-based telescopes, along with their diverse observational modes, produces images with different properties, rendering conventional methods less effective. This paper presents a novel algorithm for detecting fast-moving celestial objects within star fields. Our approach enhances state-of-the-art fast-moving celestial object detection neural networks by transforming them into physical-inspired neural networks. These neural networks leverage the point-spread function of the telescope and the specific observational mode as prior information; they can directly identify fast-moving celestial objects within star fields without requiring additional training, thereby addressing the limitations of traditional techniques. Additionally, all neural networks are integrated using the mixture-of-experts technique, forming a comprehensive fast-moving celestial object detection algorithm. We have evaluated our algorithm using simulated observational data that mimic various observations carried out by space-based telescope scenarios and real observation images. Results demonstrate that our method effectively detects fast-moving celestial objects across different observational modes and telescope configurations.