Astronomy and Astrophysics – Astronomy
Scientific paper
Nov 2007
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007aipc..954..307g&link_type=abstract
BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 27th International Workshop on Bayesian Inference and
Astronomy and Astrophysics
Astronomy
5
Markov Processes, Distribution Theory And Monte Carlo Studies, Time Series Analysis, Extrasolar Planetary Systems, Inference Methods
Scientific paper
Astronomers searching for the small signals induced by planets inevitably face significant statistical challenges. Bayesian inference has the potential of improving the interpretation of existing observations, the planning of future observations and ultimately inferences concerning the overall population of planets. This paper illustrates how a re-analysis of published radial velocity data sets with a Bayesian multi-planet Kepler periodogram is providing strong evidence for additional planetary candidates. The periodogram is implemented with a Markov chain Monte Carlo (MCMC) algorithm that employs an automated adaptive control system. For HD 11964, the data has been re-analyzed using 1, 2, 3 and 4 planet models. The most probable model exhibits three periods of 38.02-0.22+0.11, 360-4+4, and 1924-43+44 d, and eccentricities of 0.22-0.22+0.11, 0.63-0.17+0.34, and 0.05-0.05+0.03, respectively Assuming the three signals (each one consistent with a Keplerian orbit) are caused by planets, the corresponding limits on planetary mass (M sin i) and semi-major axis are (0.090-0.14+0.15MJ, 0.253-0.009+0.009au), (0.21-0.07+0.06MJ, 1.13-0.04+0.04au), (0.77-0.08+0.08MJ, 3.46-0.13+0.13au), respectively.
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