论文标题
用深度学习探测高度准直的光子喷射
Probing highly collimated photon-jets with deep learning
论文作者
论文摘要
标准模型（SM）的许多扩展可以预测在子GEV量表中存在轴突样颗粒和/或深色希格。研究了通过希格斯玻色子外来衰变产生的两个新的亚gev颗粒，一个标量和一个伪尺度。在最终状态下，这两个新粒子具有高度准直的光子的衰减特征与使用卷积神经网络和增强决策树技术的SM背景区分开。在大型强子对撞机上搜索此类物理学特征的敏感性。
Many extensions of the standard model (SM) predict the existence of axion-like particles and/or dark Higgs in the sub-GeV scale. Two new sub-GeV particles, a scalar and a pseudoscalar, produced through the Higgs boson exotic decays, are investigated. The decay signatures of these two new particles with highly collimated photons in the final states are discriminated from the ones of SM backgrounds using the Convolutional Neural Networks and Boosted Decision Trees techniques. The sensitivities of searching for such new physics signatures at the Large Hadron Collider are obtained.
