385 citations · 968 across the 23 of their papers we have counts for
23 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…
Probabilistic cosmological inference on HI tomographic data
Sambatra Andrianomena
We explore the possibility of retrieving cosmological information from 21-cm tomographic data at intermediate redshift. The first step in our approach consists of training an encod…
Timing and noise analysis of five millisecond pulsars observed with MeerKAT
M. Chisabi, S. Andrianomena, U. Enwelum +12
Millisecond pulsars (MSPs) in binary systems are precise laboratories for tests of gravity and the physics of dense matter. Their orbits can show relativistic effects that provide…
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…