Fast optics-based modeling enabling large-scale optimization of the H4 and M2 beamlines in the CERN SPS North Area
The CERN multi-purpose secondary beamlines are invaluable facilities offering a rich and diverse physics program to the international particle physics community, providing many different particle species and beam characteristics to fixed-target experiments and test-beam users. A number of magnetic elements are required to enable such flexibility, posing a significant challenge in the design and optimization of the beamline optics. In this work, we present the optimization of the H4 and M2 beamlines of the CERN SPS North Area using genetic algorithms. The aim of this study is to demonstrate the effectiveness of fast optics-based tracking models (here Xsuite), to enable large-scale, multi-objective optimization on standard computing hardware. The model is benchmarked and validated with high-fidelity Monte Carlo simulations using BDSIM and cross-validation of the results with experimental measurements. The optimized optics, applied to the H4 electron configuration for NA64, achieved a 30% increase in available electron rate and a five-fold reduction in beam-related background. In the M2 line, the optimized configurations yielded a nearly two-fold increase in electron rate and a 67% improvement in muon transmission.