Kavli Affiliate: Brian Nord
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| Summary:
Upcoming surveys are predicted to discover galaxy-scale strong lenses on the
order 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 the alternative of training only on simulated data
results in poor performance on real data. Domain Adaptation may be able to
bridge the gap between simulated and real datasets. We utilize domain
adaptation for the estimation of Einstein radius ($Theta_E$) in simulated
galaxy-scale gravitational lensing images with different levels of
observational realism. 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 of
lenses simulated to emulate noise conditions in the Dark Energy Survey (DES).
We show that both domain adaptation techniques can significantly improve the
model performance on the more complex target domain dataset. This work is the
first application of domain adaptation for a regression task in strong lensing
imaging analysis. Our results show the potential of using domain adaptation to
perform analysis of future survey data with a deep neural network trained on
simulated data.
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