Bayesian analysis of radial velocity data of GJ667C with correlated noise: evidence for only 2 planets

TL;DR

This paper introduces a Bayesian method to analyze radial velocity data with correlated noise, revealing only two confirmed planets around GJ667C and challenging previous claims of more planets due to noise assumptions.

Contribution

The paper presents a Bayesian analysis technique incorporating correlated noise and hyper-parameters, improving the accuracy of planet detection in RV data.

Findings

Confirmed two planets around GJ667C with specific periods.

Identified significant red noise with a 9-day correlation timescale.

Showed that previous claims of more planets were due to white noise assumptions.

Abstract

GJ667C is the least massive component of a triple star system which lies at a distance of about 6.8 pc (22.1 light-years) from Earth. GJ667C has received much attention recently due to the claims that it hosts up to seven planets including three super-Earths inside the habitable zone. We present a Bayesian technique for the analysis of radial velocity (RV) data-sets in the presence of correlated noise component ("red noise"), with unknown parameters. We also introduce hyper-parameters in our model in order to deal statistically with under or over-estimated error bars on measured RVs as well as inconsistencies between different data-sets. By applying this method to the RV data-set of GJ667C, we show that this data-set contains a significant correlated (red) noise component with correlation timescale for HARPS data of order 9 days. Our analysis shows that the data only provides strong evidence for the presence of two planets: GJ667Cb and c with periods 7.19d and 28.13d respectively, with some hints towards the presence of a third signal with period 91d. The planetary nature of this third signal is not clear and additional RV observations are required for its confirmation. Previous claims of the detection of additional planets in this system are due the erroneous assumption of white noise. Using the standard white noise assumption, our method leads to the detection of up to five signals in this system. We also find that with the red noise model, the measurement uncertainties from HARPS for this system are under-estimated at the level of ~50 per cent.