385 citations · 968 across the 23 of their papers we have counts for
5 papers · 1 filter
Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations
Yash Gondhalekar, Sultan Hassan, Naomi Saphra +1
The generalization of machine learning (ML) models to out-of-distribution (OOD) examples remains a key challenge in extracting information from upcoming astronomical surveys. Inter…
Radio Galaxy Zoo: Leveraging latent space representations from variational autoencoder
Sambatra Andrianomena, Hongming Tang
We propose to learn latent space representations of radio galaxies, and train a very deep variational autoencoder (\protect\Verb+VDVAE+) on RGZ DR1, an unlabeled dataset, to this e…
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…
Invertible mapping between fields in CAMELS
Sambatra Andrianomena, Sultan Hassan, Francisco Villaescusa-Navarro
We build a bijective mapping between different physical fields from hydrodynamic CAMELS simulations. We train a CycleGAN on three different setups: translating dark matter to neutr…