Application of statistical techniques to predict the effective temperature of young stars

Abstract

One of the current topics of discussion in astrophysical research revolves around the duration of the stellar cluster formation process: some theoretical models postulate formation through a single event (rapid process), others suggest multiple formation events (slow process), implying different ages for stars within the same cluster. A crucial variable for deriving age of young stars is the effective temperature. This study focuses on applying various statistical techniques, such as GLM, SVM, PLSR, Boosting, and Random Forest, to better predict the effective temperature of young stars. The results obtained from the analysis highlight that the Random Forest model outperforms other models in terms of predictive performance.