Robust marginalization of baryonic effects for cosmological inference at the field level. (arXiv:2109.10360v1 [astro-ph.CO])

We train neural networks to perform likelihood-free inference from
$(25,h^{-1}{rm Mpc})^2$ 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 ($gtrsim 100,h^{-1}{rm kpc}$) while
performing a robust marginalization over baryonic physics at the field level:
the model can infer the value of $Omega_{rm m} (pm 4%)$ and $sigma_8 (pm
2.5%)$ from simulations completely different to the ones used to train it.



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