Estimating the peak energy of Swift gamma-ray bursts using supervised machine learning

TL;DR

This paper introduces a machine learning ensemble method to accurately estimate the peak energy of Swift gamma-ray bursts, addressing the challenge of limited direct measurements.

Contribution

The study develops a SuperLearner ensemble model that improves peak energy estimation for Swift GRBs using multi-instrument data, surpassing previous Bayesian methods.

Findings

SuperLearner ensemble achieves a Pearson correlation of 0.72 with observed peak energies.

Estimated peak energies for 650 Swift GRBs, expanding the dataset for GRB studies.

Model provides more reliable estimates compared to previous Bayesian approaches.

Abstract

Gamma-ray bursts (GRBs) are among the most energetic explosive phenomena in the Universe, and their peak energy ($E_{\rm p}$) is a key physical quantity for understanding the prompt emission mechanism. However, due to the limited energy coverage of the Swift satellite, a large fraction of Swift GRBs lack reliable peak energy measurements. Therefore, developing an accurate and efficient method for estimating $E_{\rm p}$ is of great importance. In this work, we propose a method based on the SuperLearner framework that integrates multiple supervised machine learning algorithms to estimate the $E_{\rm p}$ of Swift/BAT GRBs. We used the Swift/BAT observational data from December 2004 to September 2022 as training features, and adopted 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. In 100 iterations of five-fold cross-validation, the estimated $E'_{\rm p}$ values show a tight correlation with the observed $E_{\rm p}$, with an average Pearson correlation coefficient of $r = 0.72$. Compared with previous Bayesian estimates, our model provides estimations that are likely closer to the true values. Based on the trained model, we further estimated the peak energies of 650 Swift GRBs, significantly increasing the number of GRBs with estimated peak energies and providing new statistical support for constraining GRB emission mechanisms and energy origins.