Bayesian Re-analysis of the Gliese 581 Exoplanet System
Philip C. Gregory

TL;DR
This paper applies Bayesian analysis to radial velocity data of Gliese 581, detecting five planets with high confidence and exploring the impact of stellar noise and systematic errors on planetary detection.
Contribution
It introduces a Bayesian multi-planet Kepler periodogram with a Markov chain Monte Carlo approach, including stellar jitter modeling, to re-analyze exoplanet signals.
Findings
Detected five planets in HARPS data with high confidence.
Identified only two reliable signals in HIRES data.
Additional noise modeling affects planetary detection confidence.
Abstract
A re-analysis of Gliese 581 HARPS and HIRES precision radial velocity data was carried out with a Bayesian multi-planet Kepler periodogram (from 1 to 6 planets) based on a fusion Markov chain Monte Carlo algorithm. In all cases the analysis included an unknown parameterized stellar jitter noise term. For the HARPS data set the most probable number of planetary signals detected is 5 with a Bayesian false alarm probability of 0.01. These include the , , , and d periods reported previously plus a d period. The orbital eccentricities are , , , , and , respectively. The semi-major axis and of the 5 planets are ( au, M), ( au,…
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