Fair Universe Higgs Uncertainty Challenge

作者：R. Chakkappai, W. Bhimji, P. Calafiura, Po-Wen Chang, Y. Chou, S. Diefenbacher, Jordan Dudley, Steven Farrell, A. Ghosh, Isabelle Guyon, Chris Harris, Shih-Chieh Hsu, E. Khoda, Benjamin Nachman, Peter Nugent, David Rousseau, Benjamin Thorne, Ihsan Ullah, Yulei Zhang Universit'e Paris-Saclay, CNRSIN2p3, IJCLab, Orsay, France, ChaLearn, Usa, Lawrence Berkeley National Laboratory, Berkeley, U. Washington, Seattle, U. California, Irvine, San Diego · 年份：2025 · 被引用次数：2 · 研究领域：Physics

This competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties in $(H \rightarrow \tau^+ \tau^-)$ cross-section measurement. Participants were tasked with developing advanced analysis techniques capable of dealing with uncertainties in the input training data and providing credible confidence intervals. The accuracy of these intervals was evaluated using pseudo-experiments to assess correct coverage. The dataset is now published in Zenodo, and the winning submissions are fully documented.