Detecting Extra-solar Planets with a Bayesian hybrid MCMC Kepler periodogram

TL;DR

This paper introduces a Bayesian hybrid MCMC algorithm for analyzing radial velocity data to detect additional exoplanets, improving the identification of planetary signals through advanced sampling techniques.

Contribution

It presents a novel hybrid MCMC method combining parallel tempering, simulated annealing, and genetic crossover for enhanced global minimum detection in exoplanet data analysis.

Findings

Successfully identified new planetary candidates in radial velocity data.

Demonstrated the effectiveness of the hybrid MCMC in multi-planet Kepler periodogram analysis.

Enhanced parameter estimation and model selection capabilities.

Abstract

A Bayesian re-analysis of published radial velocity data sets is providing evidence for additional planetary candidates. The nonlinear model fitting is accomplished with a new hybrid Markov chain Monte Carlo (HMCMC) algorithm which incorporates parallel tempering, simulated annealing and genetic crossover operations. Each of these features facilitate the detection of a global minimum in chi^2. By combining all three, the HMCMC greatly increases the probability of realizing this goal. When applied to the Kepler problem it acts as a powerful multi-planet Kepler periodogram for both parameter estimation and model selection. The HMCMC algorithm is embedded in a unique two stage adaptive control system that automates the tuning of the MCMC proposal distributions through an annealing operation.