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.
Related
- Impact of Rapid Acceleration on Beam Dynamics in the Rapid-Cycling Synchrotrons of a Muon Collider
- Report of the National Lab Accelerator Study Group for a Muon Collider
- Input to the SCAC Facilities Subcommittee from the U.S. Muon Collider Collaboration
- FCC-μe: High Energy, High Luminosity Muon-Electron Collider & Neutrino Factory at CERN
- Vanilla Scotogenic Model at the future Muon Collider
BibTeX
@misc{berg2026acceleration,
title = {{AI Acceleration of Muon Collider R&D: A Report from the AI4MuC Workshop}},
author = {J. Scott Berg and David Bizzozero and Tulika Bose and Lance Cooley and John Dervan and Auralee Edelen and Peter Elmer and Cristiano Fanelli and Tova Holmes and Walter Hopkins and Sergo Jindariani and Michael Kagan and Maira Khan and Young-Kee Kim and Matt LeBlanc and Lawrence Lee and Matthias Liepe and Radha Mastandrea and Patrick Meade and Federico Meloni and Chad Mitchell and Linh Nguyen and Isobel Ojalvo and Ryan Roberts and Nicholas Smith and Jean-Luc Vay and Gordon Watts and Drew Winder and Chenran Xu and Katsuya Yonehara},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.14303},
eprint = {2610.14303},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.14303},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - AI Acceleration of Muon Collider R&D: A Report from the AI4MuC Workshop
AU - J. Scott Berg
AU - David Bizzozero
AU - Tulika Bose
AU - Lance Cooley
AU - John Dervan
AU - Auralee Edelen
AU - Peter Elmer
AU - Cristiano Fanelli
AU - Tova Holmes
AU - Walter Hopkins
AU - Sergo Jindariani
AU - Michael Kagan
AU - Maira Khan
AU - Young-Kee Kim
AU - Matt LeBlanc
AU - Lawrence Lee
AU - Matthias Liepe
AU - Radha Mastandrea
AU - Patrick Meade
AU - Federico Meloni
AU - Chad Mitchell
AU - Linh Nguyen
AU - Isobel Ojalvo
AU - Ryan Roberts
AU - Nicholas Smith
AU - Jean-Luc Vay
AU - Gordon Watts
AU - Drew Winder
AU - Chenran Xu
AU - Katsuya Yonehara
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.14303
UR - https://arxiv.org/abs/2610.14303
AB - 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.
ER -