Machine learning applications in Jupiter-host star classification using stellar spectra

Abstract

ABSTRACT The link between stellar host properties, be it chemical, physical, dynamical, or galactic in nature, with the presence of planetary companions, has been one that has been repeatedly tested in the literature. Several corroborated work has argued that the correlation between a stellar atmosphere’s chemistry and the presence of gas giant companions is primordial in nature, implying that the chemical budget in a protoplanetary disc, and by proxy the eventual stellar host, increases the likelihood of gas giant formation. In this work, we aim to use the power of computer vision to build and test a machine learning classifier capable of discriminating between gas giant host stars and a comparison sample, using spectral data of the host stars in the visible regime. High-resolution spectra are used to preserve any inherent information which may contribute to the classification, and are fed into a stacked ensemble design incorporating several convolutional neural networks. The spectral range is binned such that each is assigned to a first-level voter, with the meta-learner aggregating their votes into a final classification. We contextualize and elaborate on the model design and results presented in a prior proceedings publication, and present an amended architecture incorporating semisupervized learning. Both models achieve relatively strong performance metrics and generalize over the holdout sets well, yet still present signs of overfitting.