4.7 Article

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

期刊

出版社

OXFORD UNIV PRESS
DOI: 10.1093/mnras/stt2148

关键词

methods: data analysis; methods: statistical; techniques: radial velocities; stars: individual: GJ667C; planetary systems

资金

  1. SGI/Intel
  2. HEFCE
  3. PPARC
  4. Higher Education Funding Council for England
  5. Leverhulme Trust
  6. Newton Trust
  7. STFC [ST/J005673/1, ST/K00333X/1, ST/H008586/1] Funding Source: UKRI
  8. Science and Technology Facilities Council [ST/H008586/1, ST/J005673/1, ST/K00333X/1] Funding Source: researchfish

向作者/读者索取更多资源

GJ667C is the least massive component of a triple star system which lies at a distance of about 6.8 pc (22.1 light-year) from the 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 overestimated 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 time-scale for HARPS data of the order of 9 d. Our analysis shows that the data only provide strong evidence for the presence of two planets: GJ667Cb and c with periods 7.19 and 28.13 d, respectively, with some hints towards the presence of a third signal with period 91 d. 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 underestimated at the level of similar to 50 per cent.

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