Upcoming missions to the Galilean moons will revolutionize our understanding of their interior evolution, largely governed by their orbital resonance. Yet, to fully understand the feedback between orbit and interior, crucial in determining their habitability potential, continued efforts are needed. ... Read more

In planetary geophysics, the translation of geodetic and geophysical measurements into interior properties represents a complex inverse problem (see e.g. [1-2]). This challenge is inherently characterized by non-uniqueness, mathematically correlated parameters, and the necessity for rigorous uncertainty quantification. With upcoming flagship missions—such as ESA’s Jupiter Icy Moons Explorer (Juice) [3] and NASA’s Europa Clipper [4]—preparing to deliver high-precision datasets from the Jovian system, the planetary science community requires advanced computational frameworks to interpret these observations. Historically, MCMC Bayesian inversions for constraining planetary interiors have relied on ad-hoc workflows, hard-coded parameterizations, and target-specific libraries (see e.g. [1-2]). Such an approach can be limiting, as it restricts cross-disciplinary reproducibility and scalability and a systematic comparison of analyses between different target bodies.

To overcome these limitations, we present geoMCMC, an open-source Python library developed to provide a unified, consistent, and modular computational framework for planetary interior analysis. By standardizing the inversion workflow, this library aims to enhance model sharing, cross-disciplinary collaboration, and methodological reproducibility across the planetary science community. Originally conceptualized to investigate the subsurface oceans of icy satellites [5], geoMCMC has evolved into a generalized, high-performance computational framework designed for the Bayesian inversion of both rocky and icy planetary bodies.

At its core, the software relies on a plugin-based architecture orchestrated by a central inference problem pattern. As illustrated in Fig. 1, this architecture explicitly decouples statistical inference from physical modeling through a five-pillar design: the Body (managing planetary layer states and geometry), the Parameter Set (linking statistical priors to interior attributes), the Observation Set (handling geophysical forward models), the Constraint Set (enforcing physical assumptions), and the Sampler (the Bayesian inference engine). This modularity ensures that state definitions, geometry, model constraints, and sampling algorithms function as independent, interchangeable components. For instance, users can seamlessly swap constraint modules, i.e. the functional blocks responsible for enforcing fundamental a priori physical assumptions, such as mass balance, hydrostatic equilibrium, or advanced thermodynamic models. In fact, these modules automatically resolve interdependent properties to guarantee physical consistency before any forward model is evaluated. Furthermore, the architecture is designed for high-performance parallel processing, allowing computationally intensive physical models to be offloaded to a high-efficiency compiled C++ kernel. ... Read more

The TU Delft Astrodynamics Toolbox (Tudat) is a free open-source software (FOSS) suite geared towards research and education in computational astrodynamics. It has been applied primarily to numerical simulation of the dynamics of objects in space, ranging from optimization of re-entry vehicle dynamics to the modeling of planetary spacecraft tracking and the dynamics of natural solar system bodies. The powerful and versatile estimation module of Tudat has been used for a broad range of studies for both current and future space missions. It has the capability to combine optical and radiometric tracking data from multiple spacecraft with Earth-based observations into a comprehensive estimation of the dynamics of both natural and artificial solar system bodies, as well as physical parameters of interest. Building upon this general and adaptable framework, recent developments have focused on incorporating the necessary functionality required for real tracking data analysis. In this paper, we present the integration of these capabilities into Tudat’s fully open-source framework, with a combined focus on planetary missions and Space Situational Awareness (SSA). At present, the software provides capabilities to process several categories of observational data: (i) deep-space Doppler and range tracking data of planetary missions collected by the Deep Space Network (DSN) and ESA’s ESTRACK, supporting multiple formats such as IFMS, ODF, and TNF; (ii) deep-space Doppler and VLBI tracking data of planetary missions collected by the Planetary and Radio Interferometry and Doppler Experiment (PRIDE) with radio (astronomy) telescopes; (iii) optical astrometry and radar tracking archived by the Minor Planet Center (MPC) and the Natural Satellite Data Center (NSDC). By computing observation residuals using existing orbital solutions as references, we show that our observation models are accurate to the intrinsic quality of the data (e.g., better than 0.05 mm/s for typical deep-space Doppler data). Additionally, we demonstrate that our dynamical models possess the level of fidelity necessary to enable precise orbit estimation, effectively leveraging the high quality of the available tracking data. Tudat is unique in providing modular and flexible open-source high-fidelity modeling across a broad range of orbital regimes, enabling interdisciplinary applications. We provide an overview of the data processing and estimation capabilities and give examples from various mission domains. These include high-precision orbit estimation using deep-space Doppler tracking data, orbit determination of cis-lunar/xGEO space debris in highly non-linear regimes (specifically targeting upper stages of lunar missions) from astrometric data, and estimation of small solar system bodies using astrometric data. ... Read more