November 27, 2023 to December 1, 2023
Dual node
Europe/Paris timezone

Domain Adaptation in Gravitational Lens Analysis

Not scheduled
Dual node

Dual node

IAP (Paris) & CCA/Flatiron (New York) IAP 98bis Boulevard Arago 75014 Paris FRANCE CCA/Flatiron 5th Avenue New York (NY) USA
Poster Online Posters


Paxson Swierc Yifan(Megan) Zhao


Upcoming surveys are predicted to discover galaxy-scale strong lenses on the magnitude of 10$^5$, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but training only on simulated data results in poor performance on real data. Domain adaptation can bridge the gap between simulated and real datasets. We adopt domain adaptation on the estimation of Einstein radius in simulated galaxy-scale gravitational lensing images. We evaluate two domain adaptation techniques - domain adversarial neural networks (DANN) and maximum mean discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain with emulation of DES survey conditions. We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain datasets. Our results show the potential of using domain adap- tation to perform analysis on future survey data with a deep neural network trained on simulated data.

Primary authors


Aleksandra Ciprijanovic (Fermi National Accelerator Laboratory) Brian Nord (Fermilab)

Presentation materials