https://scholars.lib.ntu.edu.tw/handle/123456789/633813
標題: | Vector boson fusion versus gluon-gluon fusion Higgs boson production with full-event deep learning: Toward a decay-agnostic tagger | 作者: | CHENG-WEI CHIANG Shih, David Wei, Shang Fu |
公開日期: | 1-一月-2023 | 卷: | 107 | 期: | 1 | 來源出版物: | Physical Review D | 摘要: | We study the benefits of jet- and event-level deep learning methods in distinguishing vector boson fusion (VBF) from gluon-gluon fusion (GGF) Higgs production at the LHC. We show that a variety of classifiers (CNNs, attention-based networks) trained on the complete low-level inputs of the full event achieve significant performance gains over shallow machine learning methods (BDTs) trained on jet kinematics and jet shapes, and we elucidate the reasons for these performance gains. Finally, we take initial steps toward the possibility of a VBF vs GGF tagger that is agnostic to the Higgs decay mode, by demonstrating that the performance of our event-level CNN does not change when the Higgs decay products are removed. These results highlight the potentially powerful benefits of event-level deep learning at the LHC. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/633813 | ISSN: | 24700010 | DOI: | 10.1103/PhysRevD.107.016014 |
顯示於: | 物理學系 |
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