HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images

Understand

Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology.

• Especially with the large number of detections in current and upcoming surveys such as the Rubin Legacy Survey of Space and Time (LSST), it is timely to investigate in automated and fast analysis techniques beyond the traditional and time consuming Markov chain Monte Carlo sampling methods.
• Building upon our convolutional neural network (CNN) presented in Schuldt et al.
• (2021b), we present here another CNN, specifically a residual neural network (ResNet), that predicts the five mass parameters of a Singular Isothermal Ellipsoid (SIE) profile (lens center $x$ and $y$, ellipticity $e_x$ and $e_y$, Einstein radius $\theta_E$) and the external shear ($\gamma_{ext,1}$, $\gamma_{ext,2}$) from ground-based imaging data.