Kavli Affiliate: Joshua Frieman
| First 5 Authors: Jason Poh, Ashwin Samudre, Aleksandra Ćiprijanović, Joshua Frieman, Gourav Khullar
| Summary:
The large number of strong lenses discoverable in future astronomical surveys
will likely enhance the value of strong gravitational lensing as a cosmic probe
of dark energy and dark matter. However, leveraging the increased statistical
power of such large samples will require further development of automated lens
modeling techniques. We show that deep learning and simulation-based inference
(SBI) methods produce informative and reliable estimates of parameter
posteriors for strong lensing systems in ground-based surveys. We present the
examination and comparison of two approaches to lens parameter estimation for
strong galaxy-galaxy lenses — Neural Posterior Estimation (NPE) and Bayesian
Neural Networks (BNNs). We perform inference on 1-, 5-, and 12-parameter lens
models for ground-based imaging data that mimics the Dark Energy Survey (DES).
We find that NPE outperforms BNNs, producing posterior distributions that are
more accurate, precise, and well-calibrated for most parameters. For the
12-parameter NPE model, the calibration is consistently within $<$10% of
optimal calibration for all parameters, while the BNN is rarely within 20% of
optimal calibration for any of the parameters. Similarly, residuals for most of
the parameters are smaller (by up to an order of magnitude) with the NPE model
than the BNN model. This work takes important steps in the systematic
comparison of methods for different levels of model complexity.
| Search Query: ArXiv Query: search_query=au:”Joshua Frieman”&id_list=&start=0&max_results=3