Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms

Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms

Identification of proton and gamma plays an essential role in ultra-high energy gamma-ray astronomy with LHAASO-KM2A. In this work, two neural networks (deep neural networks (DNN) and graph neural networks (GNN)) are applied to distinguish proton and gamma in the LHAASOKM2A simulation data. The rece...

Full description

Saved in:
Bibliographic Details
Main Author: Zhang F.
Other Authors: Mahidol University
Format: Conference or Workshop Item
Published: 2023
Subjects:
Multidisciplinary
Online Access:https://repository.li.mahidol.ac.th/handle/123456789/86591
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Mahidol University
id th-mahidol.86591
record_format dspace
spelling th-mahidol.865912023-06-19T01:06:38Z Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms Zhang F. Mahidol University Multidisciplinary Identification of proton and gamma plays an essential role in ultra-high energy gamma-ray astronomy with LHAASO-KM2A. In this work, two neural networks (deep neural networks (DNN) and graph neural networks (GNN)) are applied to distinguish proton and gamma in the LHAASOKM2A simulation data. The receiver operating characteristic (ROC) curves are used to evaluate the quality of the model. Both KM2A-DNN and KM2A-GNN models give higher Area Under Curve (AUC) scores than the traditional baseline model. 2023-06-18T18:06:38Z 2023-06-18T18:06:38Z 2022-03-18 Conference Paper Proceedings of Science Vol.395 (2022) 18248039 2-s2.0-85123976685 https://repository.li.mahidol.ac.th/handle/123456789/86591 SCOPUS
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Multidisciplinary
spellingShingle Multidisciplinary
Zhang F.
Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms
description Identification of proton and gamma plays an essential role in ultra-high energy gamma-ray astronomy with LHAASO-KM2A. In this work, two neural networks (deep neural networks (DNN) and graph neural networks (GNN)) are applied to distinguish proton and gamma in the LHAASOKM2A simulation data. The receiver operating characteristic (ROC) curves are used to evaluate the quality of the model. Both KM2A-DNN and KM2A-GNN models give higher Area Under Curve (AUC) scores than the traditional baseline model.
author2 Mahidol University
author_facet Mahidol University
Zhang F.
format Conference or Workshop Item
author Zhang F.
author_sort Zhang F.
title Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms
title_short Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms
title_full Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms
title_fullStr Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms
title_full_unstemmed Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms
title_sort identification of proton and gamma in lhaaso-km2a simulation data with deep learning algorithms
publishDate 2023
url https://repository.li.mahidol.ac.th/handle/123456789/86591
_version_ 1781415436511870976

Similar Items