Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach

In the era of mega-constellations, the need for accurate and publicly available information has become fundamental for satellite operators to guarantee the safety of spacecrafts and the Low Earth Orbit (LEO) space environment. This study critically evaluates the accuracy and reliability of publicly available ephemeris data for a LEO mega-constellation - Starlink. The goal of this work is twofold: (i) compare and analyze the quality of the data against high-precision numerical propagation. By analyzing two months of real orbital data for approximately 1500 Starlink satellites, we identify discrepancies between high precision numerical algorithms and the published ephemerides, recognizing the use of simplified dynamics at fixed thresholds, planned maneuvers, and limitations in uncertainty propagations. Furthermore, we compare data obtained from multiple sources to track and analyze deorbiting satellites over the same period. Empirically, we extract the acceleration profile of satellites during deorbiting and provide insights relating to the effects of non-conservative forces during reentry. For non-deorbiting satellites, the position Root Mean Square Error (RMSE) was approximately 300 m, while for deorbiting satellites it increased to about 600 m. Through this in-depth analysis, we highlight potential limitations in publicly available data for accurate and robust Space Situational Awareness (SSA), and importantly, we propose a data-driven model of satellite decay in mega-constellations. Keywords: Starlink, Low Earth Orbit, Physics-Informed Machine Learning, Space Situational Awareness, Satellite Decay 1. Introduction As the number of active satellites in Low Earth Orbit (LEO) continues to grow, ensuring their safe operation has become a complex challenge. Accurate trajectory prediction and collision avoidance are now essential, as overcrowding in LEO has significantly raised the likelihood of orbital collisions [1]. Such events not only threaten the functionality of space assets but also contribute to the accumulation of debris, increasing the risk of chain reaction scenarios like the Kessler syndrome [2].