Now showing items 1-2 of 2

    • Robust Field-level Likelihood-free Inference with Galaxies 

      de Santi, Natalí S. M.; Shao, Helen; Villaescusa-Navarro, Francisco; Abramo, L. Raul; Teyssier, Romain; Villanueva-Domingo, Pablo; Ni, Yueying; Anglés-Alcázar, Daniel; Genel, Shy; Hernández-Martínez, Elena; Steinwandel, Ulrich P.; Lovell, Christopher C.; Dolag, Klaus; Castro, Tiago; Vogelsberger, Mark (2023-07-18)
      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 models are rotational, translational, and ...
    • A Universal Equation to Predict Ω m from Halo and Galaxy Catalogs 

      Shao, Helen; de Santi, Natalí S. M.; Villaescusa-Navarro, Francisco; Teyssier, Romain; Ni, Yueying; Anglés-Alcázar, Daniel; Genel, Shy; Steinwandel, Ulrich P.; Hernández-Martínez, Elena; Dolag, Klaus; Lovell, Christopher C.; Garrison, Lehman H.; Visbal, Eli; Kulkarni, Mihir; Hernquist, Lars; Castro, Tiago; Vogelsberger, Mark (2023-10-18)
      We discover analytic equations that can infer the value of Ωm from the positions and velocity moduli of halo and galaxy catalogs. The equations are derived by combining a tailored graph neural network (GNN) architecture ...