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This work aims to develop a computationally inexpensive approach, based onmachine learning techniques, to accurately predict thousands of stellarrotation periods. The innovation in our approach is the use of the XGBoostalgorithm to predict the rotation periods of Kepler targets by means ofregression analysis. Therefore, we focused on building a robust supervisedmachine learning model to predict surface stellar rotation periods fromstructured data sets built from the Kepler catalogue of K and M stars. Weanalysed the set of independent variables extracted from Kepler light curvesand investigated the relationships between them and the ground truth. Using theextreme gradient boosting method, we obtained a minimal set of variables thatcan be used to build machine learning models for predicting stellar rotationperiods. Our models are validated by predicting the rotation periods of about2900 stars. The results are compatible with those obtained by classicaltechniques and comparable to those obtained by other recent machine learningapproaches, with the advantage of using much fewer predictors. Restricting theanalysis to stars with rotation periods of less than 45 days, our models are onaverage 95 to 98 % correct. We have developed an innovative approach, based ona machine learning method, to accurately fit the rotation periods of stars.Based on the results of this study, we conclude that the best models generatedby the proposed methodology are competitive with the state-of-the-artapproaches, with the advantage of being computationally cheaper, easy to train,and relying on small sets of predictors.

Predicting Stellar Rotation Periods Using XGBoost