Document Type
Article
Publication Title
Astronomical Journal
Department
Physics
ISSN
15383881
Volume
167
Issue
2
DOI
10.3847/1538-3881/ad0fe9
First Page
1
Last Page
15
Publication Date
1-19-2024
Abstract
There are more than 5000 confirmed and validated planets beyond the solar system to date, more than half of which were discovered by NASA’s Kepler mission. The catalog of Kepler’s exoplanet candidates has only been extensively analyzed under the assumption of white noise (i.i.d. Gaussian), which breaks down on timescales longer than a day due to correlated noise (point-to-point correlation) from stellar variability and instrumental effects. Statistical validation of candidate transit events becomes increasingly difficult when they are contaminated by this form of correlated noise, especially in the low-signal-to-noise (S/N) regimes occupied by Earth-Sun and Venus-Sun analogs. To diagnose small long-period, low-S/N putative transit signatures with few (roughly 3-9) observed transit-like events (e.g., Earth-Sun analogs), we model Kepler's photometric data as noise, treated as a Gaussian process, with and without the inclusion of a transit model. Nested sampling algorithms from the Python UltraNest package recover model evidences and maximum a posteriori parameter sets, allowing us to disposition transit signatures as either planet candidates or false alarms within a Bayesian framework.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Matesic, Michael R.B.; Rowe, Jason F.; Livingston, John H.; Dholakia, Shishir; Jontof-Hutter, Daniel; and Lissauer, Jack J., "Gaussian Processes and Nested Sampling Applied to Kepler's Small Long-period Exoplanet Candidates" (2024). Pacific Faculty Work. 97.
https://scholarlycommons.pacific.edu/all-faculty/97
