Deep Learning in Wide-field Surveys: Fast Analysis of Strong Lenses in
  Ground-based Cosmic Experiments
  Searches and analyses of strong gravitational lenses are challenging due to
the rarity and image complexity of these astronomical objects. Next-generation
surveys (both ground- and space-based) will provide more opportunities to
derive science from these objects, but only if they can be analyzed on
realistic time-scales. Currently, these analyses are expensive. In this work,
we present a regression analysis with uncertainty estimates using deep learning
models to measure four parameters of strong gravitational lenses in simulated
Dark Energy Survey data. Using only $gri$-band images, we predict Einstein
Radius, lens velocity dispersion, lens redshift to within $10-15\%$ of truth
values and source redshift to $30\%$ of truth values, along with predictive
uncertainties. This work helps to take a step along the path of faster analyses
of strong lenses with deep learning frameworks.
