FAIR Universe 2024: Higgs ML Uncertainty Challenge

¹ Lawrence Berkeley National Laboratory, USA
² Université Paris-Saclay, CNRS/IN2P3, IJCLab, France
³ University of Washington, Seattle, USA
⁴ University of California, Berkeley, USA
⁵ University of California, Irvine, USA
⁶ ChaLearn, USA
⁷ University of California, San Diego, USA
⁸ Université Paris-Saclay, France
⁹ Universiteit Leiden, Netherlands

Published online: 7 October 2025

Abstract

The HiggsML Uncertainty Challenge is a machine learning competition aimed at improving uncertainty-aware AI techniques in high-energy physics. Part of the FAIR Universe initiative, focuses on estimating the Higgs boson signal strength while accounting for systematic uncertainties affecting collider experiments. Unlike traditional classification tasks, participants must construct confidence intervals that properly cover systematic distortions. The HiggsML Uncertainty Challenge establishes a benchmark for uncertainty-aware AI, with applications in high-energy physics and beyond. The competition is hosted on Codabench, an open AI benchmarking platform, and uses highperformance computing resources at NERSC Perlmutter for scalable and reproducible model evaluation. The dataset and evaluation framework will remain publicly available for continued research.