| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01330 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/epjconf/202533701330 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701330
Enhancing GRB Detection: Bayesian Optimization of Triggerless Data Analysis for LHAASO-WCDA
1 Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
2 University of Chinese Academy of Sciences, 100049 Beijing, China
3 Tianfu Cosmic Ray Research Center, Chengdu, Sichuan, China
* e-mail: mustofa@ihep.ac.cn
Published online: 7 October 2025
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.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.