A Recommendation Algorithm to Predict Giant Exoplanet Host Stars Using Stellar Elemental Abundances
Natalie R. Hinkel, Cayman Unterborn, Stephen R. Kane, Garrett Somers,, and Richard Galvez

TL;DR
This paper develops a machine learning algorithm analyzing stellar elemental abundances to predict potential giant exoplanet host stars, highlighting key elements beyond iron and validating predictions with archival data.
Contribution
The study introduces a novel machine learning approach that incorporates multiple elemental abundances to improve giant exoplanet host star prediction accuracy.
Findings
Oxygen, carbon, and sodium are key indicators alongside iron.
The algorithm achieved a median 75% prediction score for known hosts.
Identified ~350 stars with high likelihood of hosting giant planets.
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…
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Taxonomy
TopicsInfrared Target Detection Methodologies · Astronomy and Astrophysical Research · Astronomical Observations and Instrumentation
