Leveraging synthetic data for star and satellite photometry

Abstract

Aperture photometry is a critical method for estimating the visual magnitudes of stars and satellites, essential in Space Domain Awareness (SDA) for tasks like collision avoidance. Traditional methods have fixed aperture shapes, limiting accuracy and adaptability. We introduce a novel approach that defines pixel-specific regions for the aperture and annulus, significantly improving accuracy. Nevertheless, conventional aperture photometry is constrained by predefined equations, leading to errors and sensitivity to image conditions. To overcome these limitations, we propose a learned photometry pipeline that combines aperture photometry with machine learning. Our approach demonstrates remarkable effectiveness for both stars and satellites across diverse image conditions. We rigorously tested it on three datasets, including a custom synthetic dataset and real imagery. Our results showcase outstanding performance, with a 1.44% error in star visual magnitude estimation and a 0.64% error in satellite visual magnitude estimation.