研究喷注分类中性能与鲁棒性的权衡，揭示高精度模型的潜在风险。
The Pareto Frontier of Resilient Jet Tagging
- 构建性能-鲁棒性帕累托前沿，系统评估不同模型的权衡表现
- 发现高精度模型在扰动下鲁棒性差，可能引入分析偏差
- 为高能物理实验提供更稳健的分类器设计思路
利用喷注内强子组分的运动学信息进行分类，是现代高能对撞机物理中的关键任务。通常分类器的设计聚焦于单一性能指标（如准确率、AUC或拒识率）以获得最佳表现。然而，仅依赖单一指标可能导致采用比其他竞争方案更依赖模型架构的方法，从而在分析中引入潜在不确定性与偏差。本文探索此类权衡，并揭示使用高性能但低鲁棒性网络的后果。
原文摘要 · Abstract (English)
Classifying hadronic jets using their constituents' kinematic information is a critical task in modern high-energy collider physics. Often, classifiers are designed by targeting the best performance using metrics such as accuracy, AUC, or rejection rates. However, the use of a single metric can lead to the use of architectures that are more model-dependent than competitive alternatives, leading to potential uncertainty and bias in analysis. We explore such trade-offs and demonstrate the consequences of using networks with high performance metrics but low resilience.
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