GPU-Based Exoplanet Transit + Noise Modelling

Michael Matesic ( Université de Montréal )

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), Matesic et. al. (2024) modeled 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 were used to 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. In order to assess larger target subsets and eventually entire catalogs, we now aim to migrate this framework to an environment which may leverage GPU-computed gradients and optimization.