Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics
This addresses the challenge of optimizing complex, stochastic programs in High Energy Physics, though it appears incremental as it applies existing gradient estimation techniques to a specific domain.
The paper tackles the problem of differentiating programs with discrete and branching randomness in High Energy Physics, enabling gradient-based optimization for tasks like detector design and simulator tuning, and develops the first fully differentiable branching program.
We propose to apply several gradient estimation techniques to enable the differentiation of programs with discrete randomness in High Energy Physics. Such programs are common in High Energy Physics due to the presence of branching processes and clustering-based analysis. Thus differentiating such programs can open the way for gradient based optimization in the context of detector design optimization, simulator tuning, or data analysis and reconstruction optimization. We discuss several possible gradient estimation strategies, including the recent Stochastic AD method, and compare them in simplified detector design experiments. In doing so we develop, to the best of our knowledge, the first fully differentiable branching program.