Searching for Additional Planets in TESS Multiplanet Systems: Testing Empirical Models Based on Kepler Data

Abstract Multiplanet system architectures are frequently used to constrain possible formation and evolutionary pathways of observed exoplanets. Therefore, understanding the predictive and descriptive power of empirical exoplanetary system models is critical to understanding their formation histories. We analyze 52 TESS multiplanet systems previously studied using D ynamite , which used TESS data alongside empirical models based on Kepler planets to predict additional planets in each system. We analyze additional TESS data to search for these predicted planets. We thereby evaluate the degree to which these models can be used to predict planets in TESS multiplanet systems. Specifically, we study whether the period ratio method or clustered period model is more predictive. We find that the period ratio model predictions are most consistent with the planets discovered since 2020, accounting for detection sensitivity. However, neither model is highly predictive, highlighting the need for additional data and more nuanced models to describe the full population. Improved eccentricity and dynamical stability prescriptions incorporated into D ynamite provide a modest improvement in the prediction accuracy. We also find that the current sample of 183 TESS multiplanet systems are highly dynamically packed, and appear truncated relative to detection biases. These attributes are consistent with the Kepler sample, and suggest an efficient formation process.