Speaker
Description
The SHiP experiment is a proposed fixed-target experiment at the CERN SPS designed to search for feebly interacting particles beyond the Standard Model. A critical challenge for SHiP is suppressing the massive flux of beam-dump muons entering the detector acceptance. The Active Muon Shield, a system of magnets designed for muon deflection, must therefore be optimized to minimize this background while satisfying strict geometric and budgetary constraints.
Conventional design optimization loops, however, are severely constrained by the computational cost of full-scale detector simulations. In this work, we bypass this bottleneck using a three-stage, GPU-accelerated framework: 1) a Normalizing Flow to rapidly sample input muon distributions, replacing Pythia8; 2) an Operator Learning model replacing finite-element magnetic field simulations; and 3) a GPU-native heuristic replacing Geant4 propagation by sampling from pre-computed interaction histograms. By accelerating simulation runtimes from months to hours without compromising fidelity, this pipeline enables the first complete optimization of the SHiP Muon Shield encompassing full muon statistics and all realistic constraints, successfully resolving the shield design challenge.