Exoplanet host star classification: Multi-Objective Optimisation of incomplete stellar abundance data

The presence of a planetary companion around its host star has been repeatedly linked with stellar properties, affecting the likelihood of sub-stellar object formation and stability in the protoplanetary disc, thus presenting a key challenge in exoplanet science. Furthermore, abundance and stellar parameter datasets tend to be incomplete, which limits the ability to infer distributional characteristics harnessing the entire dataset. This work aims to develop a methodology using machine learning and multi-objective optimisation for reliable imputation for subsequent comparison tests and host star recommendation. It integrates fuzzy clustering for imputation and ML classification of hosts and comparison stars into an evolutionary multi-objective optimisation algorithm. We test several candidates for the classification model, starting with a binary classification for giant planet hosts. Upon confirmation that the XGBoost algorithm provides the best performance, we interpret the performance of both the imputation and classification modules for binary classification. The model is extended to handle multi-label classification for low-mass planets and planet multiplicity. Constraints on the model’s use and feature/sample selection are given, outlining strengths and limitations. We conclude that the careful use of this technique for host star recommendation will be an asset to future missions and the compilation of necessary target lists.