research
∙
04/04/2023
The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
We present CAMELS-ASTRID, the third suite of hydrodynamical simulations ...
share
research
∙
02/27/2023
Robust field-level likelihood-free inference with galaxies
We train graph neural networks to perform field-level likelihood-free in...
research
∙
09/05/2022
The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
Ionized gas in the halo circumgalactic medium leaves an imprint on the c...
research
∙
01/04/2022
Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter
Complex systems (stars, supernovae, galaxies, and clusters) often exhibi...
research
∙
01/04/2022
The CAMELS project: public data release
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS...
research
∙
11/29/2021
Weighing the Milky Way and Andromeda with Artificial Intelligence
We present new constraints on the masses of the halos hosting the Milky ...
research
∙
11/16/2021
Inferring halo masses with Graph Neural Networks
Understanding the halo-galaxy connection is fundamental in order to impr...
research
∙
09/22/2021
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
We present the Cosmology and Astrophysics with MachinE Learning Simulati...
research
∙
09/21/2021
Robust marginalization of baryonic effects for cosmological inference at the field level
We train neural networks to perform likelihood-free inference from (25 h...
research
∙
09/20/2021
