FAIR Universe – the challenge of handling uncertainties in fundamental science

David Rousseau, Wahid Bhimji, Ragansu Chakkappai, Steven Farrell, Aishik Ghosh, Isabelle Guyon, Chris Harris, Elham E Khoda, Benjamin Nachman, Ihsan Ullah, Sascha Diefenbacher, Yuan-Tan Chou, Paolo Calafiura, Yulei Zheng

Keywords: Particle Physics, High Energy Physics, Uncertainties, Uncertainty-aware AI

TL;DR: Fair Universe is a scientific competition where participants have to deal with a biased dataset and estimate a confidence interval on a parameter of interest in fundamental physics.

Abstract: We propose a challenge organised in conjunction with the Fair Universe project, a collaborative effort funded by the US Department of Energy and involving the Lawrence Berkeley National Laboratory, Université Paris-Saclay, University of Washington, and ChaLearn. This initiative aims to forge an open AI ecosystem for scientific discovery. The challenge will focus on measuring the physics properties of elementary particles with imperfect simulators due to differences in modelling systematic errors. Additionally, the challenge will leverage a large-compute-scale AI platform for sharing datasets, training models, and hosting machine learning competitions. Our challenge will bring together the physics and machine learn- ing communities to advance our understanding and methodologies in handling systematic (otherwise known as epistemic) uncertainties within AI techniques.

Competition Timeline:

Public Phase June 2024-mid Oct 2024
Final Phase mid Oct 2024-end October 2024
Notification of winners end October 2024

Website: https://fair-universe.lbl.gov

Primary Contact Email: [e-mail]

Participant Contact Email: [e-mail]

Workshop Format: Hybrid (Vancouver + some online speakers)

Preferred Timezone: PT

Submission Number: 35