Year: 2026
Type: conference-paper
Abstract: The Fair Universe project organised the HiggsML Uncertainty Challenge. This groundbreaking competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties, focusing on mastering both the uncertainties in the input training data and providing credible confidence intervals in the results. The challenge revolved around measuring the $(H \rightarrow \tau^+ \tau^-)$ cross-section, similar to the HiggsML challenge held on Kaggle in 2014, using a dataset representing the 4-momentum signal state. Participants were tasked with developing advanced analysis techniques to measure the signal strength and generate confidence intervals that included both statistical and systematic uncertainties. The accuracy of these intervals was automatically evaluated using pseudo-experiments to assess correct coverage. The dataset is now published in Zenodo, and the winning submissions are fully documented.
DOI: 10.22323/1.485.0070
Authors: Ragansu Chakkappai, Wahid Bhimji, Paolo Calafiura, Po-Wen Chang, Yuan-Tang Chou, Sascha Diefenbacher, Jordan Dudley, Steven Farrell, Aishik Ghosh, Isabelle Guyon, Chris Harris, Shih-Chieh Hsu, Elham E Khoda, Benjamin Nachman, Peter Nugent, David Rousseau, Benjamin Thorne, Ihsan Ullah, Yulei Zhang
Institutions: Centre National de la Recherche Scientifique, Université Paris-Saclay, Institut National de Physique Nucléaire et de Physique des Particules, Laboratoire de Physique des 2 Infinis Irène Joliot-Curie, Lawrence Berkeley National Laboratory, University of Washington, University of California, Irvine, University of California San Diego, University of California, Berkeley
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7123597200
FWCI:
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