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arXiv 2007.14586hep-ph
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利用神经网络区分强子对撞机上的$W'$信号

Distinguishing $W'$ Signals at Hadron Colliders Using Neural Networks

Spencer Chang, Ting-Kuo Chen, Cheng-Wei Chiang

备注：
44 pages, 22 figures
发表信息：
Phys. Rev. D 103, 036016 (2021)
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摘要：

我们研究了一个基于神经网络的假设检验，以区分不同的$W'$和带电标量共振通过$\ell+\require{cancel}\cancel{E}_T$通道在强子对撞机。这在传统上是具有挑战性的，因为质子-质子对撞机（如大型强子对撞机）存在四重模糊性。在我们研究的神经网络方法中，我们发现基于全连接神经网络的多类分类器是最强大的，该神经网络是在从最终状态$\ell$的运动学变量制成的2D直方图上训练的。此外，通过考虑1-喷流过程，我们证明了可以推广到多个$2D$直方图来表示不同的变量对。最后，作为与传统方法的比较，我们将我们的方法与贝叶斯假设检验进行了比较，并讨论了每种方法的优缺点。本文提出的神经网络方案是一个强有力的工具，可以帮助探测带电共振的性质。

英文摘要：

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.

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