Speaker
Description
Experimentation at high-energy particle colliders is a well-established research field with a clear methodology and plans for large-scale experiments in the future. Its software stack includes packages developed over decades, specialized in particle collision simulation, detector simulation, data aggregation, and statistical analysis. To ensure the software's scalability for future collider experiments, the community continues to explore novel computing approaches to maximise the use of computational resources. This talk will explain possible areas where Automatic Differentiation can help in this endeavor, giving an overview of the research efforts to make parts of the HEP software stack differentiable. Particular emphasis is placed on statistical inference, where differentiable likelihoods enable significantly faster parameter optimization. As an example of a success story, this talk will present how source-code transformation AD, powered by Clad, was used to accelerate RooFit, the primary framework for statistical inference in particle physics.