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This project aims to lay the foundations for next-generation cosmic experimental design backed by new artificial intelligence and machine learning methods tailored to cosmology. The collaboration brings together computer scientists and astrophysicists working to understand how data and computation can inform and accelerate cosmic survey design, taking into account previous data, the capabilities of contemporary computational approaches, and insights from the underlying physics.

A distinguishing aspect of the project is its end-to-end focus, going from simulation to real cosmic survey in a closed loop. The work weaves together advances in deep learning, high- dimensional/nonparametric statistics, discrete optimization, and information theory; the developed methodologies will be validated in simulated environments, eventually leading to a new future cosmic experiment which will be evaluated against existing designs by Fermilab.

This project was funded by the AI + Science initiative, a program organized by the University of Chicago Office of Research and National Laboratories in collaboration with the Center for Data and Computing aimed at increasing interactions among the University of Chicago, Argonne National Laboratory, Fermi National Accelerator Laboratory, and the Toyota Technological Institute at Chicago.

Principal Investigators: Brian Nord (Fermilab), Yuxin Chen (UChicago)