Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation

Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation

Identifying gamma rays and rejecting the background of cosmic-ray hadrons are crucial for very-high-energy gamma-ray observations and relevant scientific research. Based on the simulated data from the square kilometer array (KM2A) of LHAASO, eight high-level features were extracted for the gamma/had...

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Bibliographic Details
Main Authors: Jie Li, Hongkui Lv, Yang Liu, Jiajun Huang, Yu Wang, Wenbin Lin
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:The Astrophysical Journal Supplement Series
Subjects:
High-energy cosmic radiation
Cosmic rays
Classification
Astronomy data analysis
Interdisciplinary astronomy
Astronomy software
Online Access:https://doi.org/10.3847/1538-4365/ad9581
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https://doi.org/10.3847/1538-4365/ad9581

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