- UHRA Home
- Browsing by Author
Browsing by Author "Anglés-Alcázar, Daniel"
Now showing items 1-3 of 3
-
Beware the recent past: a bias in spectral energy distribution modelling due to bursty star formation
Haskell, P.; Das, S.; Smith, D. J. B.; Cochrane, R. K.; Hayward, Christopher C.; Anglés-Alcázar, Daniel (2024-03-19)We investigate how the recovery of galaxy star formation rates (SFRs) using energy-balance spectral energy distribution (SED) fitting codes depends on their recent star formation histories (SFHs). We use the Magphys and ... -
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 ...