Abstract

Designing the next generation colliders and detectors involves solving optimization problems in high-dimensional spaces where the optimal solutions may nest in regions that even a team of expert humans would not explore. Furthermore, the large amount of data we need to generate to study physics for the next runs of large HEP machines and that we will need for future colliders is staggering, requiring rethinking of our simulation and reconstruction paradigm. Differentiable programming enables the incorporation of domain knowledge, encoded in simulation software, into gradient or reinforcement learning based pipelines, resulting in the capability of optimizing a given simulation setting and performing inference through classically intractable settings. In this talk I will describe the first proof-of-concept results for the gradient-based optimization of experimental design, with a focus on large-scale simulation software, and will briefly touch on recent advances in calorimetry with neuromorphic hardware architectures, paving the way to more complex challenges, as well as on the MODE Collaboration and the EUCAIF project.