解析极端喷流子结构
Resolving Extreme Jet Substructure
摘要：
我们研究的有效性，理论上激发的高级别喷流可观的极端情况下，喷流与大量的硬子射流（高达$N=8$）。以前的研究表明，高水平的可观测量是探测$N\le 3$硬子射流的射流子结构的强大的、可解释的工具，但是在低水平射流成分上训练的深度神经网络匹配或略微超过它们的性能。我们将这项工作扩展到$N=8$硬子射流，使用深粒子流网络（PFN）和基于Transformer的网络来估计分类性能的松散上限。对135个$\textrm{N}$ -亚喷流观测量和喷流质量的标准集合，全连接神经网络的分类精度达到86.90%，但低于PFN和Transformer模型的89.19%和91.27%，这表明组成网络利用未被该组高级可观测量捕获的信息。然后，我们识别出能够缩小这一差距的其他高级可观测量，并利用LASSO正则化进行特征选择，以识别和排名最相关的可观测量，并进一步了解基于成分的神经网络所使用的学习策略。最终的模型只包含31个高级别的可观测量，并能够匹配PFN的性能和近似的性能的Transformer模型到2%以内。
英文摘要：
We study the effectiveness of theoretically-motivated high-level jet observables in the extreme context of jets with a large number of hard sub-jets (up to $N=8$). Previous studies indicate that high-level observables are powerful, interpretable tools to probe jet substructure for $N\le 3$ hard sub-jets, but that deep neural networks trained on low-level jet constituents match or slightly exceed their performance. We extend this work for up to $N=8$ hard sub-jets, using deep particle-flow networks (PFNs) and Transformer based networks to estimate a loose upper bound on the classification performance. A fully-connected neural network operating on a standard set of high-level jet observables, 135 $\textrm{N}$-subjetiness observables and jet mass, reach classification accuracy of 86.90\%, but fall short of the PFN and Transformer models, which reach classification accuracies of 89.19\% and 91.27\% respectively, suggesting that the constituent networks utilize information not captured by the set of high-level observables. We then identify additional high-level observables which are able to narrow this gap, and utilize LASSO regularization for feature selection to identify and rank the most relevant observables and provide further insights into the learning strategies used by the constituent-based neural networks. The final model contains only 31 high-level observables and is able to match the performance of the PFN and approximate the performance of the Transformer model to within 2\%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
