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Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference
Jason Poh · Ashwin Samudre · Aleksandra Ciprijanovic · Brian Nord · Joshua Frieman · Gourav Khullar

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 successful application of Neural Density Estimators (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.

Author Information

Jason Poh (University of Chicago)

Hi! I’m a final year PhD candidate in astrophysics looking for a full time job as a data scientist or machine learning researcher. My research is at the intersection of large scale astronomy surveys and machine learning methods to analyze that survey data. I look forward to networking with you!

Ashwin Samudre (Simon Fraser University)
Aleksandra Ciprijanovic (Fermi National Accelerator Laboratory)
Brian Nord (Fermi National Accelerator Laboratory)
Joshua Frieman (University of Chicago)
Gourav Khullar (University of Pittsburgh)

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