Searching for Additional Planets in TESS Multi-Planet Systems: Testing Empirical Models Based on Kepler Data

TL;DR

This study evaluates empirical models based on Kepler data for predicting additional planets in TESS multi-planet systems, finding modest predictive success and highlighting the need for more nuanced models and data.

Contribution

The paper tests and compares period ratio and clustered period models for predicting planets in TESS systems, incorporating improved dynamical stability criteria.

Findings

Period ratio model aligns best with recent discoveries.

Neither model is highly predictive, indicating the need for better models.

TESS systems are highly dynamically packed and detection-biased.

Abstract

Multi-planet system architectures are frequently used to constrain possible formation and evolutionary pathways of observed exoplanets. Therefore, understanding the predictive and descriptive power of empirical models of these systems is critical to understanding their formation histories. Additionally, if empirical models can reproduce architectures over a range of scales, transit and radial velocity observations can be more easily and effectively used to inform future microlensing, astrometric, and direct imaging surveys. We analyze 52 TESS multi-planet systems previously studied using Dynamite (Dietrich & Apai 2020), who 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 multi-planet systems. Specifically, we study whether a 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 nuanced models to describe the full population. Improved eccentricity and dynamical stability prescriptions incorporated into Dynamite provide a modest improvement in the prediction accuracy. We also find that the current sample of 183 TESS multi-planet systems are are highly dynamically packed, and appear truncated relative to detection biases. These attributes are consistent with the Kepler sample, and suggest a highly efficient formation process.