44 citations · 67 across the 7 of their papers we have counts for
7 papers
Learning the Universe with the 2nd Generation of CAMELS: Varying 35 parameters of the IllustrisTNG model in (50Mpc/h)^3 boxes
Shy Genel, Yongseok Jo, Boon Kiat Oh +10
We present a new set of 1,192 cosmological simulations as part of the CAMELS project, in which a space of 35 cosmological, astrophysical, and numerical parameters is explored aroun…
Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
Natalí S. M. de Santi, Francisco Villaescusa-Navarro, L. Raul Abramo +11
It has been recently shown that a powerful way to constrain cosmological parameters from galaxy redshift surveys is to train graph neural networks to perform field-level likelihood…
Simulating the LOcal Web (SLOW) -- III: Synchrotron emission from the local cosmic web
Ludwig M. Böss, Klaus Dolag, Ulrich P. Steinwandel +4
Aims: Detecting diffuse synchrotron emission from the cosmic web is still a challenge for current radio telescopes. We aim to make predictions about the detectability of cosmic web…
Cosmology with one galaxy? -- The ASTRID model and robustness
Nicolas Echeverri, Francisco Villaescusa-Navarro, Chaitanya Chawak +7
Recent work has pointed out the potential existence of a tight relation between the cosmological parameter , at fixed , and the properties of individual galax…
A universal equation to predict from halo and galaxy catalogues
Helen Shao, Natalí S. M de Santi, Francisco Villaescusa-Navarro +13
We discover analytic equations that can infer the value of from the positions and velocity moduli of halo and galaxy catalogues. The equations are derived by combining…
Robust Field-level Likelihood-free Inference with Galaxies
Natalí S. M. de Santi, Helen Shao, Francisco Villaescusa-Navarro +12
We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our mode…
Robust field-level inference with dark matter halos
Helen Shao, Francisco Villaescusa-Navarro, Pablo Villanueva-Domingo +13
We train graph neural networks on halo catalogues from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogues contain…