Differentiable probabilistic programming for strong gravitational
  lensing
  The difficult task of observing Dark Matter subhaloes is of paramount
importance since it would constrain Dark Matter particle properties (cold or
warm relic) and confirm once again the longstanding $\Lambda$CDM model. In the
near future the new generation of ground and space surveys will observe
thousands of strong gravitational lensing systems providing a unique probe of
Dark Matter substructures. Here, we describe a new strong lensing analysis
pipeline that combines deep Convolutional Neural Networks with physical models
and exploits traditional sampling techniques such as Hamiltonian Monte Carlo.
Using simulated strong gravitational lensing systems, we discuss first results
and characterize the accuracy of the reconstruction of the main lensing
parameters.
