ExoFit - Characterising Orbits the Bayesian Way

Details ExoFit - Characterising Orbits the Bayesian Way ExoFit - Characterising Orbits the Bayesian Way

Sep 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011ess.....2.2007h&link_type=abstract

American Astronomical Society, ESS meeting #2, #20.07

Statistics

Scientific paper

Over 500 exoplanets have now been detected using the radial velocity technique, and various methods have been adopted to estimate their orbital parameters. On their own, or combined with data from transits, the results are used to characterise planets and their environments, and statistically to address gaps in the current understanding of planetary formation and evolution. With such an abundance of data, the need is obvious for new techniques to deal with the statistics in a rigorous and systematic fashion, and to constrain the orbital parameter space in order to push the search for new and ever smaller exoplanets.
We at UCL are developing methods borrowed from cosmology to tackle this problem in a Bayesian framework. We use the ExoFit code (Balan & Lahav 2009), utilising Markov Chain Monte Carlo (MCMC) simulations with the Metropolis-Hastings algorithm, to determine the orbital characteristics of planetary systems from radial velocity measurements. We present here the result of the application of ExoFit to the publicly-available data for over 100 stars known to host planets, approximately a quarter of the known population of planetary systems. This results in a database of uniformly-derived orbital parameters, in addition to some surprising differences between some published orbital solutions and the statistical distribution of ExoFit parameters. In addition to developments on the code (fitting for more planets, dealing with resonances etc.), this work naturally leads on to the inclusion of more planets as more data become available, resulting in an ever more comprehensive database of uniformly-derived parameters for known planetary systems.
M. Hollis is supported by an Impact/Perren studentship.

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