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We propose to apply several gradient estimation techniques to enable thedifferentiation of programs with discrete randomness in High Energy Physics.Such programs are common in High Energy Physics due to the presence ofbranching processes and clustering-based analysis. Thus differentiating suchprograms can open the way for gradient based optimization in the context ofdetector design optimization, simulator tuning, or data analysis andreconstruction optimization. We discuss several possible gradient estimationstrategies, including the recent Stochastic AD method, and compare them insimplified detector design experiments. In doing so we develop, to the best ofour knowledge, the first fully differentiable branching program.

Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics