A Bayesian Framework for Exoplanet Direct Detection and Non-detection

Jean Baptiste Ruffio, Dimitri Mawet, Ian Czekala, Bruce Macintosh, Robert J. De Rosa, Garreth Ruane, Michael Bottom, Laurent Pueyo, Jason J. Wang, Lea Hirsch, Zhaohuan Zhu, Eric L. Nielsen

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Rigorously quantifying the information in high-contrast imaging data is important for informing follow-up strategies to confirm the substellar nature of a point source, constraining theoretical models of planet-disk interactions, and deriving planet occurrence rates. However, within the exoplanet direct imaging community, non-detections have almost exclusively been defined using a frequentist detection threshold (i.e., contrast curve) and associated completeness. This can lead to conceptual inconsistencies when included in a Bayesian framework. A Bayesian upper limit is such that the true value of a parameter lies below this limit with a certain probability. The associated probability is the integral of the posterior distribution with the upper limit as the upper bound. In summary, a frequentist upper limit is a statement about the detectability of planets while a Bayesian upper limit is a statement about the probability of a parameter to lie in an interval given the data. The latter is therefore better suited for rejecting hypotheses or theoretical models based on their predictions. In this work we emphasize that Bayesian statistics and upper limits are more easily interpreted and typically more constraining than the frequentist approach. We illustrate the use of Bayesian analysis in two different cases: (1) with a known planet location where we also propose to use model comparison to constrain the astrophysical nature of the point source and (2) gap-carving planets in TW Hya. To finish, we also mention the problem of combining radial velocity and direct imaging observations.

Original languageEnglish (US)
Article number196
JournalAstronomical Journal
Issue number5
StatePublished - Nov 2018

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science


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