Robust marginalization of baryonic effects for cosmological inference at the field level

Abstract

We train neural networks to perform likelihood-free inference from $(25h^{-1}\mathrm{Mpc}{)}^2$ 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project. We show that the networks can extract information beyond one-point functions and power spectra from all resolved scales ($\gtrsim 100h^{-1}\mathrm{kpc}$) while performing a robust marginalization over baryonic physics at the field level: the model can infer the value of $\hat{I}{\text{c◯}}_{\mathrm{m}}(\pm 4\%)$ and $\ddot{I}{ƒ}_8(\pm 2.5\%)$ from simulations completely different to the ones used to train it.

7 pages, 4 figures. Second paper of a series of four. The 2D maps, codes, and network weights used in this paper are publicly available at https://camels-multifield-dataset.readthedocs.io