使用XGBoost预测恒星自转周期
Predicting Stellar Rotation Periods Using XGBoost
AI总结：
本文提出利用XGBoost回归模型，基于开普勒K型和M型恒星的光变曲线特征，以更少的预测变量和更低计算成本准确预测恒星自转周期，在周期小于45天的恒星上平均准确率达96%，性能与现有先进方法相当。
摘要：
本研究旨在开发一种基于机器学习技术的计算成本低廉的方法，以准确预测数千个恒星自转周期。我们方法的创新之处在于使用XGBoost算法，通过回归分析来预测开普勒目标的恒星自转周期。因此，我们专注于构建一个稳健的监督机器学习模型，从基于开普勒K型和M型恒星目录构建的结构化数据集中预测恒星表面自转周期。我们分析了从开普勒光变曲线中提取的自变量集合，并研究了它们与真实值之间的关系。利用极端梯度提升方法，我们获得了一组最小变量集，可用于构建预测恒星自转周期的机器学习模型。我们的模型通过预测约2900颗恒星的自转周期进行了验证。结果与经典技术获得的结果一致，并且与其他近期机器学习方法获得的结果相当，其优势在于使用的预测变量要少得多。将分析限制在自转周期小于45天的恒星上，我们的模型平均准确率为96%。我们开发了一种基于机器学习方法的创新途径，以精确拟合恒星的自转周期。基于本研究的结果，我们得出结论：所提出方法生成的最佳模型与最先进的方法具有竞争力，其优势在于计算成本更低、易于训练，并且依赖于较小的预测变量集。
英文摘要：
This work aims to develop a computationally inexpensive approach, based on machine learning techniques, to accurately predict thousands of stellar rotation periods. The innovation in our approach is the use of the XGBoost algorithm to predict the rotation periods of Kepler targets by means of regression analysis. Therefore, we focused on building a robust supervised machine learning model to predict surface stellar rotation periods from structured data sets built from the Kepler catalogue of K and M stars. We analysed the set of independent variables extracted from Kepler light curves and investigated the relationships between them and the ground truth. Using the extreme gradient boosting method, we obtained a minimal set of variables that can be used to build machine learning models for predicting stellar rotation periods. Our models are validated by predicting the rotation periods of about 2900 stars. The results are compatible with those obtained by classical techniques and comparable to those obtained by other recent machine learning approaches, with the advantage of using much fewer predictors. Restricting the analysis to stars with rotation periods of less than 45 days, our models are on average 96 % correct. We have developed an innovative approach, based on a machine learning method, to accurately fit the rotation periods of stars. Based on the results of this study, we conclude that the best models generated by the proposed methodology are competitive with the state-of-the-art approaches, with the advantage of being computationally cheaper, easy to train, and relying on small sets of predictors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
