Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data

Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data

To investigate gamma-ray bursts (GRBs) in depth, it is crucial to develop an effective method for identifying GRBs accurately. Current criteria, e.g., onboard blind search, ground blind search, and target search, are limited by manually set thresholds and perhaps miss GRBs, especially for subthresho...

Full description

Bibliographic Details
Main Authors: Peng Zhang, Bing Li, Renzhou Gui, Shaolin Xiong, Ze-Cheng Zou, Xianggao Wang, Xiaobo Li, Ce Cai, Yi Zhao, Yanqiu Zhang, Wangchen Xue, Chao Zheng, Hongyu Zhao
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:The Astrophysical Journal Supplement Series
Subjects:
Gamma-ray bursts
Convolutional neural networks
Astronomy data analysis
High energy astrophysics
Gamma-ray astronomy
Dimensionality reduction
Online Access:https://doi.org/10.3847/1538-4365/ad2de5

Internet

https://doi.org/10.3847/1538-4365/ad2de5

Similar Items