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

TL;DR

This paper introduces Nii-body, a Bayesian framework combining N-body simulations with MCMC to accurately infer parameters of multiplanet systems from observational data.

Contribution

The authors developed a novel N-body Bayesian inference tool that integrates adaptive Runge-Kutta integration with parallel tempering MCMC for exoplanet system analysis.

Findings

Demonstrated Nii-body's effectiveness on synthetic astrometric data

Showed potential for application to real observational datasets

Flexible extension to various observational methods

Abstract

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.