التفاصيل البيبلوغرافية
| العنوان: |
Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms |
| المؤلفون: |
Zhang, F., Zhu, F. R., Liu, S. M., Hao, Y. C., He, C., Hou, J., Li, Z., Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, L. X., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, H., Cai, J. T., Cao, Zhe, Chang, J., Chang, J. F., Chen, B. M., Chen, E. S., Chen, J., Chen, Liang, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, X. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, S. W., Cui, X. H., Cui, Y. D., D'Ettorre Piazzoli, B., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. J., Duan, K. K., Fan, J. H., Fan, Y. Z., Fan, Z. X., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, Y. L., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Ge, M. M., Geng, L. S., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, J. G., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. C., He, S. L., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, C., Hu, H. B., Hu, S., Hu, S. C., Hu, X. J., Huang, D. H., Huang, Q. L., Huang, W. H., Huang, X. T., Huang, X. Y., Huang, Z. C., Ji, F., Ji, X. L., Jia, H. Y., Jiang, K., Jiang, Z. J., Jin, C., Ke, T., Kuleshov, D., Levochkin, K., Li, B. B., Li, Cheng, Li, Cong, Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y., Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. S., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, W., Liu, Y., Liu, Y. N., Liu, Z. X., Long, W. J., Lu, R., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao JR(毛基荣), Masood, A., Min, Z., Mitthumsiri, W., Montaruli, T., Nan, Y. C., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Rulev, V., Sáiz, A., Shao, L., Shchegolev, O., Sheng, X. D., Shi, J. Y., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. |
| بيانات النشر: |
Sissa Medialab Srl |
| سنة النشر: |
2022 |
| المجموعة: |
Yunnan Observatories: YNAO OpenIR (Chinese Academy of Sciences, CAS) / 中国科学院云南天文台机构知识库 |
| مصطلحات موضوعية: |
天文学, 天文学::天体物理学, 天文学::天体物理学::高能天体物理学, 核科学技术, 理学, 理学::天文学, 工学, 工学::核科学与技术, Gamma rays, 461.4 Ergonomics and Human Factors Engineering - 723.4.2 Machine Learning - 931.3 Atomic and Molecular Physics - 932.1 High Energy Physics |
| الوصف: |
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. © Copyright owned by the author(s) under the terms of the Creative Commons. |
| نوع الوثيقة: |
other/unknown material |
| اللغة: |
English |
| العلاقة: |
Proceedings of Science; http://ir.ynao.ac.cn/handle/114a53/25722Test; https://pos.sissa.it/395/741Test/ |
| DOI: |
10.22323/1.395.0741 |
| الإتاحة: |
https://doi.org/10.22323/1.395.0741Test
http://ir.ynao.ac.cn/handle/114a53/25722Test
https://pos.sissa.it/395/741Test/ |
| حقوق: |
cn.org.cspace.api.content.CopyrightPolicy@7e2830be |
| رقم الانضمام: |
edsbas.2A74F912 |
| قاعدة البيانات: |
BASE |
