Asset Details
Do you wish to reserve the book?
Classifying the Cosmic-Ray Proton and Light Groups on the LHAASO-KM2A Experiment with the Graph Neural Network
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Classifying the Cosmic-Ray Proton and Light Groups on the LHAASO-KM2A Experiment with the Graph Neural Network
Do you wish to request the book?
Classifying the Cosmic-Ray Proton and Light Groups on the LHAASO-KM2A Experiment with the Graph Neural Network
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Paper
Classifying the Cosmic-Ray Proton and Light Groups on the LHAASO-KM2A Experiment with the Graph Neural Network
2019
Overview
Precise measurement about the cosmic-ray (CR) component knee is essential for revealing the mistery of CR's acceleration and propagation mechanism, as well as exploring the new physics. However, classification about the CR components is a tough task especially for the groups with the atomic number close to each other. Realizing that the deep learning has achieved a remarkable breakthrough in many fields, we seek for leveraging this technology to improve the classification performance about the CR Proton and Light groups on the LHAASO-KM2A experiment. In this work, we propose a fused Graph Neural Network model in combination of the KM2A arrays, in which the activated detectors are structured into graphs. We find that the signal and background can be effectively discriminated in this model, and its performance outperforms both the traditional physics-based method and the CNN-based model across the whole energy range.
Publisher
Cornell University Library, arXiv.org
Subject
