Deep learning approaches for jet tagging in high-energy physics are characterized as black boxes that process a large amount of information from which it is difficult to extract key distinctive observables. In this proceeding, we present an alternative to deep learning approaches, Boost Invariant Polynomials, which enables direct analysis of simple analytic expressions representing the most important features in a given task. Further, we show how this approach provides an extremely low dimensional classifier with a minimum set of features representing %effective discriminating physically relevant observables and how it consequently speeds up the algorithm execution, with relatively close performance to the algorithm using the full information.

翻译：高能物理中用于喷注标记的深度学习方法被描述为处理大量信息的黑箱，从中难以提取关键可区分可观测量。在本报告中，我们提出深度学习方法的一种替代方案——增强不变多项式，该方法能够直接分析表示给定任务中最重要特征的简单解析表达式。此外，我们展示了该方法如何构建一个极低维度的分类器，用一小组特征表示有效区分物理相关可观测量，以及它如何相应地加速算法执行，同时保持与使用全部信息的算法相对接近的性能。