In this work, supervised artificial neural networks (ANN) with rapidity–mass matrix (RMM) inputs are studied using several Monte Carlo event samples for various $pp$ collision processes. The study shows the usability of this approach for general event classification problems. The proposed standardization of the ANN feature space can simplify searches for signatures of new physics at the Large Hadron Collider (LHC) when using machine learning techniques. In particular, we illustrate how to improve signal-over-background ratios in the search for new physics, how to filter out Standard Model events for model-agnostic searches, and how to separate gluon and quark jets for Standard Model measurements.

中文翻译：

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使用快速质量矩阵的机器学习解决HEP中的事件分类问题

在这项工作中，使用多个质量的蒙特卡洛事件样本研究了带有快速质量矩阵（RMM）输入的监督人工神经网络（ANN）。 $pp$碰撞过程。研究表明，这种方法可用于一般事件分类问题。拟议的ANN特征空间标准化可以简化使用机器学习技术时在大型强子对撞机（LHC）上对新物理学签名的搜索。特别是，我们说明了如何在寻找新物理过程中提高背景信号比，如何过滤出与模型无关的标准模型事件，以及如何分离胶子和夸克喷嘴以进行标准模型测量。