%0 Generic %1 poh2022strong %A Poh, Jason %A Samudre, Ashwin %A Ćiprijanović, Aleksandra %A Nord, Brian %A Khullar, Gourav %A Tanoglidis, Dimitrios %A Frieman, Joshua A. %D 2022 %K cosmology dark_matter lensing phd %T Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference %U http://arxiv.org/abs/2211.05836 %X 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.

@misc{poh2022strong, abstract = {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.}, added-at = {2023-01-01T13:51:49.000+0100}, author = {Poh, Jason and Samudre, Ashwin and Ćiprijanović, Aleksandra and Nord, Brian and Khullar, Gourav and Tanoglidis, Dimitrios and Frieman, Joshua A.}, biburl = {https://www.bibsonomy.org/bibtex/201da956fa46273b31d28091000e113ab/intfxdx}, description = {Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference}, interhash = {8e07f534798338ba4a32adb4864c5e0f}, intrahash = {01da956fa46273b31d28091000e113ab}, keywords = {cosmology dark_matter lensing phd}, note = {cite arxiv:2211.05836Comment: Accepted to the Workshop on Machine Learning and the Physical Sciences at the 36th Conference on Neural Information Processing Systems 2022 (NeurIPS 2022)}, timestamp = {2023-01-01T13:51:49.000+0100}, title = {Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference}, url = {http://arxiv.org/abs/2211.05836}, year = 2022 }