论文标题
QCD Parton淋浴在深度学习中的影响不可见的希格斯通过矢量玻色子融合
Influence of QCD parton shower in deep learning invisible Higgs through vector boson fusion
论文作者
论文摘要
Vector Boson Fusion将自己确立为一个高度可靠的渠道，用于探测Higgs Boson和在大型强子对撞机上发现新物理学的途径。该通道在Higgs的无形衰减分支比率上提供了最严格的结合，其中当前的上限明显高于标准模型中的预期。值得注意的是，仅来自此特征性的简单过程中的低水平热量计数据可以通过采用复杂的深度学习技术来大大改善此限制。这种神经网络的构建似乎可以很好地理解事件运动学和辐射模式。但是，这种出色的能力的全部潜力还保留了QCD Parton淋浴和相应辐射模式的精确理论投影。这项工作证明了使用Parton淋浴中不同的后坐力方案和高阶计算的关系。
Vector boson fusion established itself as a highly reliable channel to probe the Higgs boson and an avenue to uncover new physics at the Large Hadron Collider. This channel provides the most stringent bound on Higgs' invisible decay branching ratio, where the current upper limits are significantly higher than the one expected in the Standard Model. It is remarkable that merely low-level calorimeter data from this characteristically simple process can improve this limit substantially by employing sophisticated deep-learning techniques. The construction of such neural networks seems to comprehend the event kinematics and radiation pattern exceptionally well. However, the full potential of this outstanding capability also warrants a precise theoretical projection of QCD parton showering and corresponding radiation pattern. This work demonstrates the relation using different recoil schemes in the parton shower with leading order and higher-order computation.
