Robust marginalization of baryonic effects for cosmological inference at the field level
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We train neural networks to perform likelihood-free inference from (25 h^-1 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 (≳ 100 h^-1 kpc) while performing a robust marginalization over baryonic physics at the field level: the model can infer the value of Ω_ m (± 4%) and σ_8 (± 2.5%) from simulations completely different to the ones used to train it.
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