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Cosmological multifield emulator
Sambatra Andrianomena, Sultan Hassan, Francisco Villaescusa-Navarro
We demonstrate the use of deep network to learn the distribution of data from state-of-the-art hydrodynamic simulations of the CAMELS project. To this end, we train a generative ad…
HIDM: Emulating Large Scale HI Maps using Score-based Diffusion Models
Sultan Hassan, Sambatra Andrianomena
Efficiently analyzing maps from upcoming large-scale surveys requires gaining direct access to a high-dimensional likelihood and generating large-scale fields with high fidelity, w…
Latent space representations of cosmological fields
Sambatra Andrianomena, Sultan Hassan
We investigate the possibility of learning the representations of cosmological multifield dataset from the CAMELS project. We train a very deep variational encoder on images which…