Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust marginalization of baryonic effects for cosmological inference at
  the field level   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.

13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th