Nii-body: Bayesian Inference of Multiplanet Dynamics via N-body Simulations
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913021657350144 |
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| author | Jia, Hong-Fei Jin, Sheng Wu, Dong-Hong Liu, Shang-Fei |
| author_facet | Jia, Hong-Fei Jin, Sheng Wu, Dong-Hong Liu, Shang-Fei |
| contents | 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 (RV), astrometry, and transit timing variations (TTVs) -- requires N-body integration. Motivated by this need, we developed a Bayesian fitting framework that couples N-body integration with Markov chain Monte Carlo (MCMC) to retrieve the system parameters of multiplanet systems. The code, named \texttt{Nii-body}, integrates an adaptive Runge--Kutta--Fehlberg 7(8) (RKF78) solver with an automated parallel tempering MCMC algorithm. Using simplified synthetic astrometric observations, we evaluated the efficiency and robustness of \texttt{Nii-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 RV, TTVs, or combined datasets, providing a versatile engine for high-precision orbital inference in multiplanet systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09383 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Nii-body: Bayesian Inference of Multiplanet Dynamics via N-body Simulations Jia, Hong-Fei Jin, Sheng Wu, Dong-Hong Liu, Shang-Fei Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics 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 (RV), astrometry, and transit timing variations (TTVs) -- requires N-body integration. Motivated by this need, we developed a Bayesian fitting framework that couples N-body integration with Markov chain Monte Carlo (MCMC) to retrieve the system parameters of multiplanet systems. The code, named \texttt{Nii-body}, integrates an adaptive Runge--Kutta--Fehlberg 7(8) (RKF78) solver with an automated parallel tempering MCMC algorithm. Using simplified synthetic astrometric observations, we evaluated the efficiency and robustness of \texttt{Nii-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 RV, TTVs, or combined datasets, providing a versatile engine for high-precision orbital inference in multiplanet systems. |
| title | Nii-body: Bayesian Inference of Multiplanet Dynamics via N-body Simulations |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2604.09383 |