A Bayesian Framework for Exoplanet Direct Detection and Non-detection

Authors

Jean-Baptiste Ruffio, Stanford UnivesityFollow
Dimitri Mawet, California Institute of TechnologyFollow
Ian Czekala, Stanford University; KIPAC Postdoctoral Fellow
Bruce Macintosh, Stanford University
Robert J. De Rosa, University of California, Berkeley
Garreth Ruane, California Institute of Technology; NSF FellowFollow
Michael Bottom, California Institute of TechnologyFollow
Laurent Pueyo, Space Telescope Science Institute
Jason J. Wang, University of California, Berkeley
Lea Hirsch, University of California, Berkeley
Zhaohuan Zhu, University of Nevada, Las VegasFollow
Eric L. Nielsen, Stanford University

Document Type

Article

Publication Date

10-12-2018

Publication Title

Astronomical Journal

Volume

156

Issue

5

First page number:

1

Last page number:

16

Abstract

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.

Keywords

Instrumentation: Adaptive optics; Instrumentation: High angular resolution; Methods: Statistical; Planetary systems; Planet-disk interactions; Planets and satellites: Detection

Disciplines

Astrophysics and Astronomy

File Format

pdf

File Size

4.511 Kb

Language

English

Repository Citation

Ruffio, J., Mawet, D., Czekala, I., Macintosh, B., De Rosa, R. J., Ruane, G., Bottom, M., Pueyo, L., Wang, J. J., Hirsch, L., Zhu, Z., Nielsen, E. L. (2018). A Bayesian Framework for Exoplanet Direct Detection and Non-detection. Astronomical Journal, 156(5), 1-16.
http://dx.doi.org/10.3847/1538-3881/aade95