We train neural networks to perform likelihood-free inference from (25h−1Mpc)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 (≳100h−1kpc) while performing a robust marginalization over baryonic physics at the field level: the model can infer the value of Ωm(±4%) and σ8(±2.5%) from simulations completely different to the ones used to train it.
Reference:
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, Second paper of a series of four. The 2D maps, codes, and network weights used in this paper are publicly available at this https URL, arXiv:2109.10360