Speaker
Description
Applying automatic differentiation (AD) to particle simulations such as Geant4 opens the possibility of gradient-based optimization for detector design and parameter tuning in high-energy physics. In this talk, we extend our previous work on differentiable Geant4 simulations by incorporating multiple Coulomb scattering into the physics model, moving closer to realistic detector modeling. The inclusion of multiple scattering introduces substantial challenges for differentiation, due to increased stochasticity. We study these effects in detail and demonstrate stable derivatives of a Geant simulation. As a concrete application, we perform gradient-based optimization of a realistic sampling calorimeter using full electromagnetic physics. These results represent an important step toward practical, large-scale detector optimization with complete Geant4 electromagnetic simulations.