Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations

no code implementations • 29 Nov 2023 • Yash Gondhalekar, Sultan Hassan, Naomi Saphra, Sambatra Andrianomena

The generalization of machine learning (ML) models to out-of-distribution (OOD) examples remains a key challenge in extracting information from upcoming astronomical surveys.

Inductive Bias Out-of-Distribution Generalization

Constraining the astrophysics and cosmology from 21cm tomography using deep learning with the SKA

no code implementations • 17 Jul 2019 • Sultan Hassan, Sambatra Andrianomena, Caitlin Doughty

Future Square Kilometre Array (SKA) surveys are expected to generate huge datasets of 21cm maps on cosmological scales from the Epoch of Reionization (EoR).

Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics

Predicting the Neutral Hydrogen Content of Galaxies From Optical Data Using Machine Learning

no code implementations • 22 Mar 2018 • Mika Rafieferantsoa, Sambatra Andrianomena, Romeel Davé

When we train on mock data from Mufasa and test on RESOLVE, this increases to {\sc rmse}$\sim0. 45$.

Astrophysics of Galaxies