Kavli Affiliate: Brian D. Nord
| First 5 Authors: Paxson Swierc, Marcos Tamargo-Arizmendi, Aleksandra Ćiprijanović, Brian D. Nord,
| Summary:
Modeling strong gravitational lenses is prohibitively expensive for modern
and next-generation cosmic survey data. Neural posterior estimation (NPE), a
simulation-based inference (SBI) approach, has been studied as an avenue for
efficient analysis of strong lensing data. However, NPE has not been
demonstrated to perform well on out-of-domain target data — e.g., when trained
on simulated data and then applied to real, observational data. In this work,
we perform the first study of the efficacy of NPE in combination with
unsupervised domain adaptation (UDA). The source domain is noiseless, and the
target domain has noise mimicking modern cosmology surveys. We find that
combining UDA and NPE improves the accuracy of the inference by 1-2 orders of
magnitude and significantly improves the posterior coverage over an NPE model
without UDA. We anticipate that this combination of approaches will help enable
future applications of NPE models to real observational data.
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