期刊
ASTRONOMICAL JOURNAL
卷 159, 期 2, 页码 -出版社
IOP PUBLISHING LTD
DOI: 10.3847/1538-3881/ab5190
关键词
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资金
- CIERA
- Data Science Initiative at Northwestern University
- Office of the Provost
- Office for Research
- Northwestern University Information Technology
- Institute for CyberScience
- Center for Exoplanets and Habitable Worlds - The Pennsylvania State University
- Eberly College of Science
- Pennsylvania Space Grant Consortium
- NSF [1616086]
- NASA Exoplanets Research Program [NNX15AE21G]
- CONICYT-Chile [Basal-CATA PFB-06/2007]
- FONDECYT [3160439]
- Ministry of Economy, Development, and Tourism's Millennium Science Initiative [IC120009]
- DFG cluster of excellence Origin and Structure of the Universe
- National Science and Engineering Research Council of Canada
- Fundacao para a Ciencia e Tecnologia (FCT, Portugal) [SFRH/BD/93848/2013]
- POPH/FSE (EC)
- FCT
- FEDER through COMPETE2020 [UID/FIS/04434/2013, POCI-01-0145FEDER-007672, PTDC/FIS-AST/1526/2014, POCI01-0145-FEDER-016886]
- National Centre for Competence in Research PlanetS of the Swiss National Science Foundation (SNSF)
- NASA's Nexus for Exoplanet System Science (NExSS)
- Division Of Astronomical Sciences
- Direct For Mathematical & Physical Scien [1616086] Funding Source: National Science Foundation
- Fundação para a Ciência e a Tecnologia [SFRH/BD/93848/2013] Funding Source: FCT
We present results from a data challenge posed to the radial velocity (RV) community: namely, to quantify the Bayesian evidence for n = {0, 1, 2, 3} planets in a set of synthetically generated RV data sets containing a range of planet signals. Participating teams were provided the same likelihood function and set of priors to use in their analysis. They applied a variety of methods to estimate , the marginal likelihood for each n-planet model, including cross-validation, the Laplace approximation, importance sampling, and nested sampling. We found the dispersion in n-planet models: similar to 3 for zero planets, similar to 10 for one planet, similar to 10(2)-10(3) for two planets, and >10(4) for three planets. Most internal estimates of uncertainty in for individual methods significantly underestimated the observed dispersion across all methods. Methods that adopted a Monte Carlo approach by comparing estimates from multiple runs yielded plausible uncertainties. Finally, two classes of numerical algorithms (those based on importance and nested samplers) arrived at similar conclusions regarding the ratio of n- and (n + 1)-planet models. One analytic method (the Laplace approximation) demonstrated comparable performance. We express both optimism and caution: we demonstrate that it is practical to perform rigorous Bayesian model comparison for models of <= 3 planets, yet robust planet discoveries require researchers to better understand the uncertainty in and its connections to model selection.
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