{"ID":2867321,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19431","arxiv_id":"2509.19431","title":"The Pareto Frontier of Resilient Jet Tagging","abstract":"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.","short_abstract":"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 archi...","url_abs":"https://arxiv.org/abs/2509.19431","url_pdf":"https://arxiv.org/pdf/2509.19431v2","authors":"[\"Rikab Gambhir\",\"Matt LeBlanc\",\"Yuanchen Zhou\"]","published":"2025-09-23T18:00:01Z","proceeding":"hep-ph","tasks":"[\"hep-ph\",\"cs.LG\",\"hep-ex\"]","methods":"[]","has_code":false}