Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/epjconf/202533701330/pdf

Authors: Abdulhafiz Ahmed Mustofa, Yaodong Cheng, Zhiguo Yao

Institutions: Chinese Academy of Sciences, Institute of High Energy Physics, University of Chinese Academy of Sciences

Language: English

Topic: Gamma-ray bursts and supernovae

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: Alliance of International Science Organizations

OpenAlex ID: W7127112055

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