A Statistical Comparative Planetology Approach to Maximize the Scientific Return of Future Exoplanet Characterization Efforts
Abstract
Provided that sufficient resources are deployed, we can look forward to an extraordinary future in which we will characterize potentially habitable planets. Until now, we have had to base interpretations of observations on habitability hypotheses that have remained untested. To test these theories observationally, we propose a statistical comparative planetology approach to questions of planetary habitability. The key objective of this approach will be to make quick and cheap measurements of critical planetary characteristics on a large sample of exoplanets, exploiting statistical marginalization to answer broad habitability questions. This relaxes the requirement of obtaining multiple types of data for a given planet, as it allows us to test a given hypothesis from only one type of measurement using the power of an ensemble. This approach contrasts with a "systems science" approach, where a few planets would be extensively studied with many types of measurements. A systems science approach is associated with a number of difficulties which may limit overall scientific return, including: the limited spectral coverage and noise of instruments, the diversity of exoplanets, and the extensive list of potential false negatives and false positives. A statistical approach could also be complementary to a systems science framework by providing context to interpret extensive measurements on planets of particular interest. We strongly recommend future missions with a focus on exoplanet characterization, and with the capability to study large numbers of planets in a homogenous way, rather than exclusively small, intense studies directed at a small sample of planets.
Keywords
Cite
@article{arxiv.1903.05211,
title = {A Statistical Comparative Planetology Approach to Maximize the Scientific Return of Future Exoplanet Characterization Efforts},
author = {Jade H. Checlair and Dorian S. Abbot and Robert J. Webber and Y. Katherina Feng and Jacob L. Bean and Edward W. Schwieterman and Christopher C. Stark and Tyler D. Robinson and Eliza Kempton and Olivia D. N. Alcabes and Daniel Apai and Giada Arney and Nicolas Cowan and Shawn Domagal-Goldman and Chuanfei Dong and David P. Fleming and Yuka Fujii and R. J. Graham and Scott D. Guzewich and Yasuhiro Hasegawa and Benjamin P. C. Hayworth and Stephen R. Kane and Edwin S. Kite and Thaddeus D. Komacek and Ravi K. Kopparapu and Megan Mansfield and Nadejda Marounina and Benjamin T. Montet and Stephanie L. Olson and Adiv Paradise and Predrag Popovic and Benjamin V. Rackham and Ramses M. Ramirez and Gioia Rau and Chris Reinhard and Joe Renaud and Leslie Rogers and Lucianne M. Walkowicz and Alexandra Warren and Eric. T. Wolf},
journal= {arXiv preprint arXiv:1903.05211},
year = {2019}
}
Comments
White paper submitted in response to the solicitation of feedback for the "2020 Decadal Survey" by the National Academy of Sciences