论文标题
使用神经网络区分强子小组的$ w'U信号
Distinguishing $W'$ Signals at Hadron Colliders Using Neural Networks
论文作者
论文摘要
我们研究了基于神经网络的假设测试，以通过$ \ ell++require {cancel} \ cancel {e} _t $ Channel在HADRON COLLIDERS上区分不同的$ W'$并通过$ \ ELL+\ requient {cancel} \ CANCEL。传统上，由于质子 - 普罗顿山脉（例如大型强子对撞机）的歧义，这在传统上具有挑战性。在我们研究的神经网络方法中，我们发现了一个基于完全连接的神经网络的多级分类器，该神经网络是根据最终状态$ \ ell $的2D直方图训练的，该分类器是最强大的2D直方图。此外，通过考虑1射流过程，我们证明一个人可以推广到多个$ 2D $直方图以表示不同的变量对。最后，作为与传统方法的比较，我们将方法与贝叶斯假设检验进行了比较，并讨论了每种方法的利弊。本文介绍的神经网络方案是一种强大的工具，可以帮助探测带电共振的属性。
We investigate a neural network-based hypothesis test to distinguish different $W'$ and charged scalar resonances through the $\ell+\require{cancel}\cancel{E}_T$ channel at hadron colliders. This is traditionally challenging due to a four-fold ambiguity at proton-proton colliders, such as the Large Hadron Collider. Of the neural network approaches we studied, we find a multi-class classifier based on a fully-connected neural network trained upon 2D histograms made from kinematic variables of the final state $\ell$ to be the most powerful. Furthermore, by considering the 1-jet processes, we demonstrate that one can generalize to multiple $2D$ histograms to represent different variable pairs. Finally, as a comparison to traditional approaches, we compare our method with Bayesian hypothesis testing and discuss the pros and cons of each approach. The neural network scheme presented in this paper is a powerful tool that can help probe the properties of charged resonances.
