AI 中文总结
该研究针对粒子物理喷注标记问题，基于Transformer生成模型拓展最优极限研究至W、Z、H→gg喷注，发现极限差距具喷注依赖性，为标记器性能优化提供依据。
AI 中文摘要
喷注标记即确定高能强子喷注的起源，是粒子物理中的关键挑战。基于机器学习的标记器已取得显著进展，引发当前方法距理论性能极限有多近的问题。此前研究针对增强顶夸克喷注，采用基于Transformer的生成模型，该模型能提供具有已知概率密度函数的真实合成喷注数据，可直接对比现代标记器与最优似然比分类器。本文总结该方法，并将研究扩展至增强W、Z及H→gg喷注。研究发现，与估计最优极限的差距具有强喷注依赖性，且在这些看似更具挑战性的标记任务中大幅缩小。还简要讨论了旨在理解这些极限的可解释性、鲁棒性及缩放性的 ongoing 工作。
英文摘要
Jet tagging, i.e. determining the origin of high-energy hadronic jets, is a key challenge in particle physics. Machine-learning-based taggers have achieved remarkable progress, raising the question of how close current methods are to the theoretical limit of performance. Previous work addressed this question for boosted top-quark jets using transformer-based generative models that provide realistic synthetic jet data with known probability density functions. This enables a direct comparison between modern taggers and the optimal likelihood-ratio classifier. In this note, we summarize the approach and extend the study to boosted W, Z, and H$\rightarrow gg$ jets. We find that the gap to the estimated optimal limit is strongly jet dependent and is substantially reduced for these seemingly more challenging tagging tasks. Ongoing work aimed at understanding the interpretation, robustness, and scaling of these limits is also briefly discussed.
CommentsProceedings contribution to ACAT 2025
