Speakers
Jonas Rembser
(CERN)
Vassil Vasilev
(Princeton University (US))
Description
In this presentation, we make the case for differential programming in C++ for High Energy Physics. We will first introduce source code-transformation-based Automatic Differentiation (AD) with Clad, a Clang compiler plugin. Some success stories of how this is used for statistical analysis in ROOT are presented, including using Clad to differentiate through statistical likelihoods in RooFit and neural network inference with TMVA SOFIE. Finally, we are reporting on some toy studies on end-to-end differentiable analysis pipelines and how such studies could guide differentiable algorithm development and help identify the “killer app” for differentiable programming in HEP.