FAIR Universe HiggsML Uncertainty Dataset and Competition
Wahid Bhimji, Ragansu Chakkappai, Po-Wen Chang, Yuan-Tang Chou, Sascha Diefenbacher, Jordan Dudley, Ibrahim Elsharkawy, Steven Farrell, Aishik Ghosh, Cristina Giordano, Isabelle Guyon, Chris Harris, Yota Hashizume, Shih-Chieh Hsu, Elham E Khoda, Claudius Krause, Ang Li, Benjamin Nachman, David Rousseau, Robert Schöfbeck, Maryam Shooshtari, Dennis Schwarz, Ihsan Ullah, Daohan Wang, Yulei Zhang
The FAIR Universe – HiggsML Uncertainty Challenge focuses on measuring the physics properties of elementary particles with imperfect simulators due to differences in modelling systematic errors.  Participants were required to compute and report confidence intervals for a parameter of interest regarding the Higgs boson while accounting for various uncertainties. The dataset is a tabular dataset of 28 features and 220 million instances. Each instance represents a simulated proton-proton collision as observed at CERN's Large Hadron Collider in Geneva. The features of these simulations capture the characteristics of various signature particles. These features include primary attributes, such as the energy and three-dimensional direction of the particles, as well as derived attributes, which are calculated from the primary ones using domain-specific knowledge. Additionally, a label feature designates the type of proton-proton collision for each example, distinguishing the Higgs boson of interest from three other background sources.  The permanent release of the dataset, as outlined in this paper, allows long-term benchmarking of new techniques. The leading submissions are described, including Contrastive Normalising Flows and Density Ratios estimation through classification. Our challenge has brought together the physics and machine learning communities to advance our understanding and methodologies in handling systematic (epistemic) uncertainties within AI techniques.