AI Acceleration of Muon Collider R&D: A Report from the AI4MuC Workshop

Abstract

A Muon Collider is one of the most attractive routes to multi-TeV lepton collisions, but it requires the accelerator and detector communities to solve a tightly coupled set of problems that have no close precedent in an operating collider. Muons must be produced, captured, cooled, accelerated, and collided before they decay. The detector must then extract clean physics from an environment dominated by decay products from the beamline. Artificial intelligence is not a substitute for accelerator physics, detector design, or engineering validation. It is, however, unusually well matched to several of the central obstacles: high-dimensional design spaces, expensive simulations, coupled subsystem trade-offs, and workflows that are currently too slow or too fragile to support broad design exploration. This report distills the AI4MuC workshop into a summary of AI use cases with the potential to change the pace of Muon Collider R&D, organized around the following physics bottlenecks: muon production and capture, ionization cooling, magnet and RF technology, detector design in beam-induced background, background simulation and generation, reconstruction, and software.