论文标题
BIP：增强不变多项式用于有效的喷气标记
BIP: Boost Invariant Polynomials for Efficient Jet Tagging
论文作者
论文摘要
深度学习方法正在成为高能量物理（HEP）数据分析的首选方法。但是，大多数以物理风格的现代体系结构在计算上效率低下，缺乏解释性。 Jet标记算法尤其如此，考虑到现代粒子探测器产生的大量数据，计算效率至关重要。在这项工作中，我们为喷气式代表介绍了一个新颖，多功能和透明的框架。 Lorentz Group Boost不变，这在喷射标记基准测试基准上的准确性很高，同时比其他现代方法更快地训练和评估了训练和评估的阶数。
Deep Learning approaches are becoming the go-to methods for data analysis in High Energy Physics (HEP). Nonetheless, most physics-inspired modern architectures are computationally inefficient and lack interpretability. This is especially the case with jet tagging algorithms, where computational efficiency is crucial considering the large amounts of data produced by modern particle detectors. In this work, we present a novel, versatile and transparent framework for jet representation; invariant to Lorentz group boosts, which achieves high accuracy on jet tagging benchmarks while being orders of magnitudes faster to train and evaluate than other modern approaches for both supervised and unsupervised schemes.
