11 citations · 28 across the 6 of their papers we have counts for
6 papers
Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models
Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Pablo Araya-Araya +8
Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions. They ha…
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…
The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
Yueying Ni, Shy Genel, Daniel Anglés-Alcázar +12
We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets tha…
High-fidelity reproduction of central galaxy joint distributions with Neural Networks
Natália V. N. Rodrigues, Natalí S. M. de Santi, Antonio D. Montero-Dorta +1
The relationship between galaxies and haloes is central to the description of galaxy formation, and a fundamental step towards extracting precise cosmological information from gala…
Improving cosmological covariance matrices with machine learning
Natalí S. M. de Santi, L. Raul Abramo
Cosmological covariance matrices are fundamental for parameter inference, since they are responsible for propagating uncertainties from the data down to the model parameters. Howev…
Mimicking the halo-galaxy connection using machine learning
Natalí S. M. de Santi, Natália V. N. Rodrigues, Antonio D. Montero-Dorta +3
Elucidating the connection between the properties of galaxies and the properties of their hosting haloes is a key element in galaxy formation. When the spatial distribution of obje…