AI Framework Improves Gamma-Ray Burst Light Curve Reconstruction

The development of a multi-model framework for reconstructing Gamma-ray burst (GRB) light curves represents a significant advancement in the field of high-energy astrophysics. Mitigating data gaps in GRB light curves is crucial for accurate cosmological research, as it enhances the precision of parameters derived from the light curves. The incorporation of seven different models, including deep learning techniques and traditional methods, demonstrates a comprehensive approach to the problem. The finding that Quartic Smoothing Spline (QSS) significantly reduces uncertainty across key parameters is particularly noteworthy. The CNN-BiLSTM model's low outlier rate for alpha further highlights the potential of machine learning in GRB light curve analysis. These advancements broaden the application of machine-learning techniques in GRB LC analysis, enhancing uncertainty estimation and parameter recovery. The framework's ability to improve the applicability of the Dainotti relation, which connects the rest-frame end time of the plateau emission and its luminosity, is a valuable contribution. Overall, this research supports the role of GRBs as cosmological probes and standard candles, facilitating GRB redshift predictions through advanced machine-learning approaches.

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