Enhancing GRB Detection: Bayesian Optimization of Triggerless Data Analysis for LHAASO-WCDA

Gamma-Ray Bursts (GRBs) are among the universe’s most energetic events, requiring advanced methods to separate signals from background noise. The LHAASO-WCDA is well-suited for detecting very-high-energy (VHE) gamma rays, but traditional trigger-based methods struggle with lowenergy GRBs. This study optimizes a triggerless detection algorithm using Bayesian optimization, enhancing sensitivity and noise suppression. Applied to GRB 221009A, our approach achieved 11.5σ in triggerless data, revealing low-energy gamma rays below the 100 GeV threshold that were previously undetectable by trigger methods. These results demonstrate the potential of the triggerless method to expand the detection capabilities of LHAASO-WCDA and improve GRB studies in high-energy astrophysics.