1 citations · 1 across the 4 of their papers we have counts for
6 papers
Towards Mitigating Systematics in Large-Scale Surveys via Few-Shot Optimal Transport-Based Feature Alignment
Sultan Hassan, Sambatra Andrianomena, Benjamin D. Wandelt
Systematics contaminate observables, leading to distribution shifts relative to theoretically simulated signals-posing a major challenge for using pre-trained models to label such…
Towards cosmological inference on unlabeled out-of-distribution HI observational data
Sambatra Andrianomena, Sultan Hassan
We present an approach that can be utilized in order to account for the covariate shift between two datasets of the same observable with different distributions. This helps improve…
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…
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…
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…