Speaker
Description
Fast parametric detector simulations transform generator-level particles into reconstructed physics objects through a chain of modules controlled by smearing functions. A smearing function contains closed-form resolution and efficiency formulae with numeric coefficients which are traditionally tuned by hand against full simulation or data. We study gradient-based optimization of Delphes3, a multipurpose fast detector simulator for phenomenology studies, using a novel differentiable workflow that can optimize the full set of simulator parameters. We compare this approach to a surrogate network which combines simulation and reconstruction steps. The two workflows provide complementary views on the accuracy and interpretability of fast simulators for the LHC and future experiments.