Application of deep learning methods to high-energy astrophysics  

作　　者：Ziwei Ou 

机构地区：[1]Tsung-Dao Lee Institute,Shanghai Jiao Tong University,Shanghai 201210,China

出　　处：《Astronomical Techniques and Instruments》2025年第1期44-51,共8页天文技术与仪器(英文)

基　　金：supported by the Natural Science Foundation of China(12393853)。

摘　　要：High-energy gamma-ray astronomy,at frequencies of 100 MeV to 100 GeV,yields insights into the fields of compact objects,extreme processes,and particle propagation.Thousands of gamma-ray sources have been detected by the Fermi Gamma-ray Space Telescope,many without any known counterpart at other wavelengths or clear identification of the source.Deep learning algorithms have been successfully applied to a variety of problems in astronomy.In this paper,I give some typical examples for classifying Fermi sources with deep learning methods,to show how such techniques can improve capability to unveil the nature of high-energy gamma-ray sources.

关 键 词：Gamma-ray astronomy Pulsar Active galactic nucleus Deep learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] P14[自动化与计算机技术—控制科学与工程]