近日，日本粒子与核研究所理论中心的A. Hammad及其研究团队取得一项新进展。经过不懈努力，他们提出射流尖头结构辅助的流线型射流标记网络。相关研究成果已于2024年6月26日在国际知名学术期刊《高能物理杂志》上发表。

在本文中，研究人员使用MLP混合器引入了一个新的射流分类网络，其中两个后续MLP操作用于在射流成分上转换粒子和特征令牌。为确保网络在射流成分排列变化时保持性能稳定，研究人员运用了多头交叉注意力机制，将转换后的粒子信息与子射流信息高效结合。研究人员使用两种聚类算法来识别子射流：具有固定半径参数的标准顺序重组算法，和新的基于HDBSCAN的IRC安全的基于密度的动态半径算法。

该研究所提出的网络显示出与最先进的模型相当的分类性能，同时大大提高了计算效率。最后，研究人员使用各种可解释的方法评估网络性能，包括集中校准和注意力映射，以突出网络在对撞机分析任务中的有效性。

据悉，基于注意力的变压器模型在对撞机分析中变得越来越普遍，为射流标记等任务提供了增强的性能。然而，它们是计算密集型的，需要大量的数据进行训练。

附：英文原文

Title: Streamlined jet tagging network assisted by jet prong structure

Author: Hammad, A., Nojiri, Mihoko M.

Issue&Volume: 2024-06-26

Abstract: Attention-based transformer models have become increasingly prevalent in collider analysis, offering enhanced performance for tasks such as jet tagging. However, they are computationally intensive and require substantial data for training. In this paper, we introduce a new jet classification network using an MLP mixer, where two subsequent MLP operations serve to transform particle and feature tokens over the jet constituents. The transformed particles are combined with subjet information using multi-head cross-attention so that the network is invariant under the permutation of the jet constituents. We utilize two clustering algorithms to identify subjets: the standard sequential recombination algorithms with fixed radius parameters and a new IRC-safe, density-based algorithm of dynamic radii based on HDBSCAN. The proposed network demonstrates comparable classification performance to state-of-the-art models while boosting computational efficiency drastically. Finally, we evaluate the network performance using various interpretable methods, including centred kernel alignment and attention maps, to highlight network efficacy in collider analysis tasks.

DOI: 10.1007/JHEP06(2024)176

Source: https://link.springer.com/article/10.1007/JHEP06(2024)176