Kavli Affiliate: Brian D. Nord
| First 5 Authors: Shrihan Agarwal, Aleksandra Ćiprijanović, Brian D. Nord, ,
| Summary:
Modeling strong gravitational lenses is computationally expensive for the
complex data from modern and next-generation cosmic surveys. Deep learning has
emerged as a promising approach for finding lenses and predicting lensing
parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a
common approach for obtaining aleatoric (data) uncertainties from a neural
network prediction. However, neural networks have not been demonstrated to
perform well on out-of-domain target data successfully – e.g., when trained on
simulated data and applied to real, observational data. In this work, we
perform the first study of the efficacy of MVEs in combination with
unsupervised domain adaptation (UDA) on strong lensing data. The source domain
data is noiseless, and the target domain data has noise mimicking modern
cosmology surveys. We find that adding UDA to MVE increases the accuracy on the
target data by a factor of about two over an MVE model without UDA. Including
UDA also permits much more well-calibrated aleatoric uncertainty predictions.
Advancements in this approach may enable future applications of MVE models to
real observational data.
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