Transfer learning made cosmology 10× cheaper — then started confusing new physics with old
A paper published in the Journal of Cosmology and Astroparticle Physics on June 10 asks a deceptively simple question: if you train a neural network on the standard cosmological model and then ask it to look for physics beyond that model, does the pretraining help or hurt? The answer, from a Princeton and Flatiron Institute team, is both. Transfer learning — pretraining on cheap ΛCDM simulations, then fine-tuning on expensive beyond-ΛCDM runs — cut the number of simulations needed by more than a factor of 10 in some scenarios. But it also introduced a failure mode the authors call negative transfer: the network learned the standard model’s patterns so thoroughly that it confused genuinely new physics with familiar old parameters. ...
