Estimating the peak energy of Swift GRBs

However, due to the limited energy coverage of the Swift satellite, a large fraction of Swift GRBs lack reliable measurements of the peak energy. Therefore, developing an accurate and efficient method to predict Ep is of great importance.

In this work, we propose a method based on the SuperLearner framework that integrates multiple supervised machine learning algorithms to predict Ep of Swift/BAT GRBs. We use the Swift/BAT observational data from December 2004 to September 2022 as training features, and adopt the peak energies of 516 GRBs jointly detected by Swift and either Fermi/GBM or Konus-Wind as training labels. After training and testing multiple supervised models, the final SuperLearner ensemble yields a more robust and reliable predictive model.