This competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties in (H→τ+τ−) 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.
Cite
@article{arxiv.2509.22247,
title = {Fair Universe Higgs Uncertainty Challenge},
author = {Ragansu Chakkappai and Wahid Bhimji and Paolo Calafiura and Po-Wen Chang and Yuan-Tang Chou and Sascha Diefenbacher and Jordan Dudley and Steven Farrell and Aishik Ghosh and Isabelle Guyon and Chris Harris and Shih-Chieh Hsu and Elham E. Khoda and Benjamin Nachman and Peter Nugent and David Rousseau and Benjamin Thorne and Ihsan Ullah and Yulei Zhang},
journal= {arXiv preprint arXiv:2509.22247},
year = {2026}
}