AI learns the cosmos, but bias may hide new physics
Researchers trained an AI on standard ΛCDM cosmology simulations and tested its ability to probe beyond the standard model. Transfer learning reduced the number of simulations needed, but the AI developed negative transfer biases, mistaking known patterns for new effects and risking missed clues about new physics. The team emphasizes careful interpretation and plans to test the approach on more realistic survey data to determine when AI can reliably accelerate cosmological discovery.