论文标题
VBF与GGF Higgs具有全面的深度学习：朝着腐烂的agnovnostic tagger
VBF vs. GGF Higgs with Full-Event Deep Learning: Towards a Decay-Agnostic Tagger
论文作者
论文摘要
我们研究了LHC在LHC的GLUON-GLUON FUSION（GGF）HIGGS生产中，将喷气和事件级深度学习方法与矢量玻色融合（VBF）区分开。我们表明，在完整事件的完整低级输入中训练了各种分类器（CNN，基于注意力的网络），对对喷气运动学和喷气形状进行培训的浅机器学习方法（BDT）实现了显着的性能提高，我们阐明了这些性能提高的原因。最后，通过证明当Higgs衰减产品被删除时，我们采取了对HIGGS衰减模式不可知的VBF与GGF Tagger的可能性，该步骤不可知。这些结果突出了LHC事件级深度学习的潜在强大好处。
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 towards 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.
