Kavli Affiliate: Joshua A. Frieman
| First 5 Authors: Jason Poh, Ashwin Samudre, Aleksandra Ćiprijanović, Brian Nord, Gourav Khullar
| Summary:
Current ground-based cosmological surveys, such as the Dark Energy Survey
(DES), are predicted to discover thousands of galaxy-scale strong lenses, while
future surveys, such as the Vera Rubin Observatory Legacy Survey of Space and
Time (LSST) will increase that number by 1-2 orders of magnitude. The large
number of strong lenses discoverable in future surveys will make strong lensing
a highly competitive and complementary cosmic probe.
To leverage the increased statistical power of the lenses that will be
discovered through upcoming surveys, automated lens analysis techniques are
necessary. We present two Simulation-Based Inference (SBI) approaches for lens
parameter estimation of galaxy-galaxy lenses. We demonstrate the successful
application of Neural Posterior Estimation (NPE) to automate the inference of a
12-parameter lens mass model for DES-like ground-based imaging data. We compare
our NPE constraints to a Bayesian Neural Network (BNN) and find that it
outperforms the BNN, producing posterior distributions that are for the most
part both more accurate and more precise; in particular, several source-light
model parameters are systematically biased in the BNN implementation.
| Search Query: ArXiv Query: search_query=au:”Joshua A. Frieman”&id_list=&start=0&max_results=3