Speaker
Description
Algorithmic differentiation (AD) allows to compute derivative of
computer-implemented function. Among other applications, such
derivatives are useful across domains for gradient-based design
optimization and parameter fitting. In the context of high-energy
physics, AD may allow to systematically improve detector designs based on end-to-end simulations of detectors. We have recently added
an important building block to this end by releasing a forward-mode
differentiated version of the Geant4 toolkit for the simulation of the
passage of particles through matter, validating derivatives for a
simple sampling calorimeter made up of square layers of absorber and
gap material.
In this poster, we use the differentiated version of Geant4 to simulate
a calorimeter made up of a 100x100x100 grid of sensitive voxels. This
allows to validate the derivatives investigated in our previous study,
namely, derivatives of energy depositions with respect to the primary
energy of incoming particles and geometric lengths of the detector
design, in this more complex setup.
