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
Abstract
The FAIR Universe – HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to compute and report confidence intervals for a parameter of interest regarding the Higgs boson while accounting for various systematic (epistemic) uncertainties. The dataset is a tabular dataset of 28 features and 280 million instances. Each instance represents a simulated proton-proton collision as observed at CERN’s Large Hadron Collider in Geneva, Switzerland. The features of these simulations were chosen to capture key characteristics of different types of particles. These include primary attributes, such as the energy and three-dimensional momentum of the particles, as well as derived attributes, which are calculated from the primary ones using domain-specific knowledge. Additionally, a label feature designates each instance’s type of proton-proton collision, distinguishing the Higgs boson events of interest from three background sources. As outlined in this paper, the permanent dataset release allows long-term benchmarking of new techniques. The leading submissions, including Contrastive Normalising Flows and Density Ratios estimation through classification, are described. Our challenge has brought together the physics and machine learning communities to advance our understanding and methodologies in handling systematic uncertainties within AI techniques.
BibTeX
@inproceedings{
bhimji2025fair,
title={{FAIR} Universe Higgs{ML} Uncertainty Dataset and Competition},
author={Wahid Bhimji and Ragansu Chakkappai and Po-Wen Chang and Yuan-Tang Chou and Sascha Diefenbacher and Jordan Dudley and Ibrahim Elsharkawy and Steven Farrell and Aishik Ghosh and Cristina Giordano and Isabelle Guyon and Chris Harris and Yota Hashizume and Shih-Chieh Hsu and Elham E Khoda and Claudius Krause and Ang Li and Benjamin Nachman and David Rousseau and Robert Sch{\"o}fbeck and Maryam Shooshtari and Dennis Schwarz and Ihsan Ullah and Daohan Wang and Yulei Zhang},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2025},
url={https://openreview.net/forum?id=F48NfH4NFE}
}