Machine Learning-based Light Curves Brightness Prediction for Space Objects in the Geostationary Belt

Predicting the brightness of a space object is essential to ensure it can be visible from telescopes in the future and eventually also to reconstruct its light curve. Indeed, the analysis of satellite light curves, which represent the variation in brightness as a function of the phase angle or time, can be helpful to retrieve information about the object, such as its attitude, shape and configuration. This paper employs the light curves of some GEO satellites collected by the Space4 Center telescopes at the University of Arizona to generate a training dataset for neural networks. Specifically, the input of the neural network consists of the day of the year when the light curve was acquired as well as the longitudinal phase angle and the type of satellite, whereas the output is the brightness. Different types of training are carried out, considering first the type of satellite as input and then also the type of bus. Results show that recurrent neural networks are able to perform an accurate regression of the given light curves and predict the brightness. This framework will potentially be useful to synthetically generate several realistic light curves for more accurate neural network training, without the necessity to actually perform the observations through the telescopes.