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We train neural networks to perform likelihood-free inference from$(25\,h^{-1}{\rm Mpc})^2$ 2D maps containing the total mass surface densityfrom thousands of hydrodynamic simulations of the CAMELS project. We show thatthe networks can extract information beyond one-point functions and powerspectra from all resolved scales ($\gtrsim 100\,h^{-1}{\rm kpc}$) whileperforming a robust marginalization over baryonic physics at the field level:the model can infer the value of $\Omega_{\rm m} (\pm 4\%)$ and $\sigma_8 (\pm2.5\%)$ from simulations completely different to the ones used to train it.

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