Refining Non-Conservative Force Modeling of LRO for Long-Term Lunar Gravity Signals

Since 2009, the Lunar Reconnaissance Orbiter (LRO) has been mapping the Moon to unprecedented detail, capturing, among others, high-resolution images and altimetric profiles to acquire invaluable datasets for understanding its evolution. Transforming this wealth of data into detailed maps and terrain models depends on the accurate determination of the spacecraft's trajectory. This is achieved through a precise orbit determination process, which relies on radio tracking data acquired by ground stations. Furthermore, the orbit determination of LRO can allow scientists to refine estimates of the Moon's geophysical parameters (e.g., gravity field, tidal response), advancing our understanding of its internal structure and history (Goosens et al., 2024; Mazarico et al.,2014).

The reliability of these estimates is intrinsically tied to the accuracy of the spacecraft's orbit reconstruction. LRO's motion is influenced by various perturbative forces, among which non-conservative forces, such as the pressure exerted by solar or planetary radiation, pose significant challenges. These forces, typically small in magnitude, are complex to model accurately. An incorrect modeling of non-gravitational effects can introduce errors in orbit determination that build up over time, leading to biases in scientific measurements and potentially resulting in incorrect interpretations of the Moon's geophysical properties.

To mitigate the errors introduced by mismodelling, a multi-arc approach is typically employed in the orbit determination process, dividing the mission timeline into shorter arcs. However, this approach reduces sensitivity to long-term gravitational signals, such as those originating from the Moon’s inner core. By refining the spacecraft's dynamical model, it becomes possible to extend the duration of the arcs, potentially enabling the recovery of previously undetected signals and a better understanding of the Moon’s interior. The availability of LRO’s extensive radiometric data, recorded during intervals unaffected by wheel off-loading maneuvers, offers an ideal dataset for developing and testing more refined physical-numerical models.

This work focuses on enhancing the modeling of non-gravitational accelerations acting on the LRO to detect long-term lunar gravity signals. To address this task, we test innovative modelling techniques based on ray-tracing methods and compare them against traditional approaches to evaluate their accuracy and effectiveness. Our results show that the ray-tracing is a powerful tool to refine the dynamical model of the spacecraft for planetary geodesy and geophysics investigations. This framework not only helps obtaining an accurate trajectory reconstruction but also provides a means for gaining deeper insights into the Moon's internal dynamics, contributing to a more comprehensive understanding of its geophysical and evolutionary processes.