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arXiv · 2109.10360

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

Francisco Villaescusa-Navarro·Shy Genel·Daniel Angles-Alcazar·David N. Spergel·Yin Li·Benjamin Wandelt·Leander Thiele·Andrina Nicola·Jose Manuel Zorrilla Matilla·Helen Shao·Sultan Hassan·Desika Narayanan·Romeel Dave·Mark Vogelsberger
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Abstract

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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Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar, David N. Spergel, Yin Li, Benjamin Wandelt, Leander Thiele, Andrina Nicola, Jose Manuel Zorrilla Matilla, Helen Shao, Sultan Hassan, Desika Narayanan, Romeel Dave, Mark Vogelsberger. 2021-09-21. Robust marginalization of baryonic effects for cosmological inference at the field level. https://arxiv.org/abs/2109.10360

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