Deep inference of simulated strong lenses in ground-based surveys

TL;DR

This paper demonstrates that deep learning, specifically Neural Posterior Estimation, provides accurate and well-calibrated parameter inference for strong gravitational lensing systems in ground-based astronomical surveys, outperforming Bayesian Neural Networks.

Contribution

It compares two deep learning approaches, NPE and BNNs, for lens parameter estimation, showing NPE's superior accuracy and calibration in simulated ground-based survey data.

Findings

NPE outperforms BNNs in accuracy and calibration.

Calibration within 10% for NPE across all parameters.

Residual errors are significantly smaller with NPE.

Abstract

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.