Nii: a Bayesian orbit retrieval code applied to differential astrometry

TL;DR

Nii is an open-source Python tool that uses Bayesian methods and parallel tempering MCMC to accurately retrieve exoplanet orbital parameters from differential astrometry data, demonstrated on single and dual-planet systems.

Contribution

The paper introduces Nii, a novel Bayesian orbit retrieval code with an automatic parallel tempering MCMC strategy, tailored for differential astrometry exoplanet detection.

Findings

Efficient convergence on posterior distributions in simulated systems.

Versatility of Nii for different planetary system configurations.

Open-source implementation for broader Bayesian analysis applications.

Abstract

Here we present an open source Python-based Bayesian orbit retrieval code (Nii) that implements an automatic parallel tempering Markov chain Monte Carlo (APT-MCMC) strategy. Nii provides a module to simulate the observations of a space-based astrometry mission in the search for exoplanets, a signal extraction process for differential astrometric measurements using multiple reference stars, and an orbital parameter retrieval framework using APT-MCMC. We further verify the orbit retrieval ability of the code through two examples corresponding to a single-planet system and a dual-planet system. In both cases, efficient convergence on the posterior probability distribution can be achieved. Although this code specifically focuses on the orbital parameter retrieval problem of differential astrometry, Nii can also be widely used in other Bayesian analysis applications.