FAIR Universe – handling uncertainties and distribution shifts for precision cosmology

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 fundamental properties of the universe from weak gravitational lensing datasets with imperfect simulators and potential distribution shifts. 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 learning communities to advance our understanding and methodologies in handling systematic (otherwise known as epistemic) uncertainties and distribution shifts within AI techniques.