Nii-body: Bayesian Inference of Multiplanet Dynamics in N-body Simulations

Many exoplanetary systems are multiplanet configurations whose long-term dynamics are governed by N-body gravitational interactions. Consequently, their detection signatures cannot be adequately described by Keplerian orbits. Accurately interpreting the observational data of these systems, including radial velocity, astrometry, and transit timing variations, requires N-body integration. To address this, we have developed a Bayesian fitting framework that couples N-body integration with Markov chain Monte Carlo, to retrieve the system parameters of multiplanet systems. The code, named \mathrmNii-body, integrates an adaptive Runge–Kutta–Fehlberg 7(8) solver with an automated parallel tempering Markov chain Monte Carlo algorithm. Using simplified synthetic astrometric observations, we evaluated the efficiency and robustness of \mathrmNii-body's N-body orbit retrieval on an idealized two-planet model, demonstrating its potential for future application to real observational data. The N-body fitting workflow can be readily extended to radial velocity, transit timing variations, or combined datasets, providing a versatile engine for high-precision orbital inference in multiplanet systems.