papersSEP 10 04:00 UTC
ML models with different inductive biases tested for cosmological inference from galaxy catalogs
Researchers used simulated galaxy catalogs from the CAMELS hydrodynamic simulations to infer the matter density parameter with machine learning models that embody different inductive biases. The paper evaluates how these architectural assumptions shape field-level likelihood-free inference of cosmological parameters.