The General Open Orbital Dynamics (GOOD) Platform: Open-Source and Reproducible Orbit Propagation and Parameter Estimation for Solar-System Science

Orbital dynamics analyses are crucial for a wide range of solar-system science investigations, including spacecraft radio-science data analysis for planetary geodesy and small-body orbit estimation and prediction applications in both solar-system evolution and planetary defense. Yet, many of the software systems and data-analysis pipelines used for orbit propagation and orbit estimation are closed-source, application-specific, or difficult to reproduce outside the originating team. This limits independent validation, reuse of previous analyses, systematic combination of heterogeneous data types, and broader community participation.

Here, we present our project to set up the General Open Orbital Dynamics (GOOD) platform, an open-science infrastructure that will combine the TU Delft Astrodynamics Toolbox (Tudat) [1,2] with the University of Groningen’s WISE-based data-handling technology [3]. The platform is funded by OpenScienceNL, a Dutch national programme supporting the transition to open science through targeted funding, policy development, and community building; its initial development phase will run until late 2029.

Tudat provides open-source high-fidelity numerical propagation and estimation capabilities for spacecraft and natural solar-system bodies. WISE provides scalable astronomical data handling, with detailed data lineage and reproducible processing workflows. By integrating these two systems, GOOD will provide an open platform on which researchers, data providers, and citizen-science contributors can run, validate, archive, retrieve, and update orbit-dynamics analyses together with their input data, configuration files, software versions, and output products. The resulting platform is intended to function not only as a repository for data products, but as an executable and traceable analysis environment in which complete orbit-dynamics workflows can be rerun, inspected, modified, and extended, with full access to all associated data and source code.

Tudat is already used by a growing research community for planetary mission analysis, natural-body ephemeris estimation, and space situational awareness. However, transparently and consistently sharing complete analysis setups, input data, processing histories, and resulting products remains difficult in practice. WISE directly provides this missing layer through its data-centric architecture for scalable data handling, provenance tracking, and reproducible workflows, making the integration of Tudat and WISE a core aspect of realizing the GOOD platform. This will establish a framework through which previous analyses become more accessible to the community, while new analyses become easier to configure, validate, and reproduce.

Our vision is to have a platform where the full analysis setup, including all data, scripts and files needed to reproduce a published analysis, is retrievable by a single web-based request or terminal command. The platform will be seeded by the proposal team and their scientific networks, using the numerous analyses currently ongoing (see for instance [4][5][6][7][8] in these proceedings). Each analysis will be packaged with its input data, model settings, processing history, and results, and will be assigned persistent identifiers where appropriate. By archiving analyses performed using the platform within a consistent configuration framework, GOOD will allow users to continue from previous work rather than reconstructing complete processing chains from publications, local scripts, or undocumented assumptions.

Beyond making existing analyses available, the data-lineage functionality of the WISE-based technology will support automatic updates of archived analyses when additional tracking data or new calibration products become available. This will help keep archived analyses traceable and up to date as improved inputs are released. The platform will also support automatic generation of skeleton configurations for new analyses, based on user-provided targets, data types, dynamical models, and desired accuracy. This will facilitate the process of setting up analyses of new targets or datasets by limiting the effort required from the researcher/user to those aspects that are very specific to their application, while generic steps such as data retrieval, pre-processing, configuration validation, and workflow execution are automated where possible. Finally, while the platform will primarily use existing data sources such as PDS, PSA, MPC, as well as astrometric data from Gaia and Euclid, it will also provide the option for users to upload their own data, for both their own analyses and reuse by others

The scope of the project includes the processing and modelling of spacecraft radio-tracking data (see [9] in these proceedings for specific developments made for our platform), optical Earth- and space-based astrometry, stellar occultations and radar ranging. Example applications include the analysis of planetary mission tracking data, improved ephemerides of natural satellites, asteroid orbit determination and prediction, and automated orbit estimation of Earth-orbiting objects/debris for Space Situational Awareness. Given the broad scope of the project, we will also cover applications on the interface of typical analysis regimes, such as the orbit determination of cislunar space debris. The same infrastructure will also support adjacent applications in space situational awareness and near-Earth-object monitoring, where reproducible estimation and uncertainty quantification are similarly critical.

A central aim of GOOD is not to replace existing tools or databases, but to make orbit-dynamics analyses more transparent, interoperable, and reusable. The platform will adopt existing standards where available, provide translation layers to external tools and data formats where possible, and define open interfaces for community contribution. It will be accessible through both web-based and terminal-based interfaces, with documentation, tutorials, issue tracking, and training material developed alongside the technical infrastructure.

At EPSC, we will present the scientific motivation, preliminary results, planned architecture, initial use cases, and development roadmap for GOOD. We will also seek input from the planetary-science community on priority datasets, model requirements, configuration standards, and potential contributed use cases. The long-term goal is to make orbit propagation, orbit estimation, and associated planetary-science data products open, reproducible, and reusable by design.

 

[1] Dirkx, D., et al. (2022), EPSC2022-253

[2] Gisolfi, L, et al. (2025), arXiv preprint arXiv:2510.23179

[3] Begeman et al. (2013), Exp Astron 35,1–23

[4] Hener, J., et al. (2026), (these proceedings)

[5] Reichel, M., et al. (2026), (these proceedings)

[6] Rodriguez Sanchez, A., et al. (2026), (these proceedings)

[7] Revellino M. M. et al. (2026), (these proceedings)

[8] Attree N. et al. (2026), (these proceedings)

[9] Hinüber. et al. (2026), (these proceedings)