A Recommendation Algorithm to Predict Giant Exoplanet Host Stars Using Stellar Elemental Abundances

Abstract

The presence of certain elements within a star, and by extension its planet, strongly impacts the formation and evolution of the planetary system. The positive correlation between a host star's iron-content and the presence of an orbiting giant exoplanet has been confirmed; however, the importance of other elements in predicting giant planet occurrence is less certain despite their central role in shaping internal planetary structure. We designed and applied a machine learning algorithm to the Hypatia Catalog (Hinkel et a. 2014) to analyze the stellar abundance patterns of known host stars to determine those elements important in identifying potential giant exoplanet host stars. We analyzed a variety of different elements ensembles, namely volatiles, lithophiles, siderophiles, and Fe. We show that the relative abundances of oxygen, carbon, and sodium, in addition to iron, are influential indicators of the presence of a giant planet. We demonstrate the predictive power of our algorithm by analyzing stars with known giant planets and found that they had median 75% prediction score. We present a list of ~350 stars with no currently discovered planets that have a $\geq$90% prediction probability likelihood of hosting a giant exoplanet. We investigated archival HARPS data and found significant trends that HIP62345, HIP71803, and HIP10278 host long-period giant planet companions with estimated minimum $M_p\sin(i)$ values of 3.7, 6.8, and 8.5 M$_{J}$, respectively. We anticipate that our findings will revolutionize future target selection, the role that elements play in giant planet formation, and the determination of giant planet interior structure models.