Type: Poster
Session: Posters (Monday & Tuesday)
Author: Logan O'Brien

Abstract: Rubin is expected to discover  ~103 galaxy-scale strongly lensed AGN systems. We are using a machine learning method, neural posterior estimation, to automatically model these systems with the intention of using them for time delay cosmography, to constrain the Hubble constant and, ultimately, the properties of Dark Energy. In the experiment shown here, our neural network is trained on simulated lens systems with simple mass distributions that consist of a single central deflector. We are testing the robustness of our network’s ability to make predictions for complex mass models by adding a perturbing mass to the lens plane in the mock LSST data test set.  The presence of the perturber introduces a bias in H0 of ~6%, while reducing the model precision by a factor of 1.25. This indicates the need to train our network with a more complex, realistic mass model.