NextGeneration Celestial Pole Osset Prediction

• Autores: Belda, S.; Karbon, M.; Modiri, S.; Ferrándiz, J.M.
• Año de publicación: 2025
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Astronómicas; Earth Orientation Parameters (EOP); Celestial Pole Offsets (CPO); Machine learning prediction
• Institución: Universidad Nacional de La Plata

The growing reliance of modern society on geodetic applications-with stringent demands for precision and long-term stability-underscores the critical role of accurate Earth Orientation Parameter (EOP) predictions. These parameters form the backbone of the Global Geodetic Observing System (GGOS) under the International Association of Geodesy (IAG), enabling reliable alignment between the International Celestial Reference Frame (ICRF) and the International Terrestrial Reference Frame (ITRF). Accurate EOP predictions are thus vital for a wide range of applications including satellite orbit determination, autonomous navigation, precision agriculture, and timing systems. Among EOPs, the Celestial Pole Offsets (CPO) remain a challenge, as their accurate determination relies exclusively on Very Long Baseline Interferometry (VLBI) observations. CPO comprise the Free Core Nutation (FCN), long-term trends, harmonics induced by deficiencies in the IAU 2006/2000A precession-nutation model, geophysical excitations, and observational noise. In this work, we advance the prediction of CPO by integrating updated precession-nutation models, newly estimated FCN representations, and modern machine learning techniques. The FCN models are derived from high-precision VLBI observations using refined parametrization strategies that capture the complex geophysical dynamics more accurately. Furthermore, machine learning algorithms are employed to model residual patterns and nonlinear behaviors in the CPO time series, enhancing short- and mid-term predictive capabilities. This hybrid approach bridges empirical modeling with data-driven insights, resulting in substantial improvements in prediction accuracy and contributing to the long-term stability and precision goals set by GGOS.
Facultad de Ciencias Astronómicas y Geofísicas