3 papers
astro-ph.IM2025
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…
astro-ph.IM2025
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…
astro-ph.CO2024
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…