# FAIR Universe HiggsML Uncertainty Dataset and Competition

arXiv: https://arxiv.org/abs/2410.02867 · HTML: https://mlsanity.com/paper/2410.02867/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 91 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 27 of 27)

- [Approximating Likelihood Ratios with Calibrated Discriminative Classifiers](https://mlsanity.com/paper/1506.02169/) (2015)
- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform](https://mlsanity.com/paper/2110.05802/) (2021)
- [Mining gold from implicit models to improve likelihood-free inference](https://mlsanity.com/paper/1805.12244/) (2018)
- [Learning to Pivot with Adversarial Networks](https://mlsanity.com/paper/1611.01046/) (2016)
- [Parameterized Machine Learning for High-Energy Physics](https://mlsanity.com/paper/1601.07913/) (2016)
- [MadMiner: Machine learning-based inference for particle physics](https://mlsanity.com/paper/1907.10621/) (2019)
- [Constraining Effective Field Theories with Machine Learning](https://mlsanity.com/paper/1805.00013/) (2018)
- [A Guide to Constraining Effective Field Theories with Machine Learning](https://mlsanity.com/paper/1805.00020/) (2018)
- [Shifts 2.0: Extending The Dataset of Real Distributional Shifts](https://mlsanity.com/paper/2206.15407/) (2022)
- [Understanding Event-Generation Networks via Uncertainties](https://mlsanity.com/paper/2104.04543/) (2021)
- [INFERNO: Inference-Aware Neural Optimisation](https://mlsanity.com/paper/1806.04743/) (2018)
- [Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019](https://mlsanity.com/paper/2201.03801/) (2022)
- [Decorrelated Jet Substructure Tagging using Adversarial Neural Networks](https://mlsanity.com/paper/1703.03507/) (2017)
- [The Tracking Machine Learning challenge : Throughput phase](https://mlsanity.com/paper/2105.01160/) (2021)
- [A Cautionary Tale of Decorrelating Theory Uncertainties](https://mlsanity.com/paper/2109.08159/) (2021)
- [neos: End-to-End-Optimised Summary Statistics for High Energy Physics](https://mlsanity.com/paper/2203.05570/) (2022)
- [Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification](https://mlsanity.com/paper/2206.08138/) (2022)
- [Adversarial domain adaptation to reduce sample bias of a high energy physics classifier](https://mlsanity.com/paper/2005.00568/) (2020)
- [Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review](https://mlsanity.com/paper/2007.09121/) (2020)
- [Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters](https://mlsanity.com/paper/2003.07186/) (2020)
- [Interpretable Uncertainty Quantification in AI for HEP](https://mlsanity.com/paper/2208.03284/) (2022)
- [Enhanced Higgs to τ^+τ^- Searches with Deep Learning](https://mlsanity.com/paper/1410.3469/) (2014)
- [Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference](https://mlsanity.com/paper/2110.00449/) (2021)
- [Learning Optimal Test Statistics in the Presence of Nuisance Parameters](https://mlsanity.com/paper/2203.13079/) (2022)
- [Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data](https://mlsanity.com/paper/2208.14024/) (2022)
- [NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results](https://mlsanity.com/paper/2208.14686/) (2022)

## Built on by (most cited first, 2 of 2)

- [Building Machine Learning Challenges for Anomaly Detection in Science](https://mlsanity.com/paper/2503.02112/) (2025)
- [Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge](https://mlsanity.com/paper/2605.08518/) (2026)

## Abstract (arXiv)

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 release of the dataset 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.