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. We extend our previous work on differentiable Geant 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 with full EM physics, performing gradient-based optimization of a realistic sampling calorimeter. In this talk, we will highlight results so far in differentiable Geant, discuss lessons learned, and provide an outlook for further studies.