Abstract
Abstract
One of the key design choices of any sampling calorimeter is
how fine to make the longitudinal and transverse segmentation. To
inform this choice, we study the impact of calorimeter segmentation
on energy reconstruction. To ensure that the trends are due
entirely to hardware and not to a sub-optimal use of segmentation,
we deploy deep neural networks to perform the reconstruction. These
networks make use of all available information by representing the
calorimeter as a point cloud. To demonstrate our approach, we
simulate a detector similar to the forward calorimeter system
intended for use in the ePIC detector, which will operate at the
upcoming Electron Ion Collider. We find that for the energy
estimation of isolated charged pion showers, relatively fine
longitudinal segmentation is key to achieving an energy resolution
that is better than 10% across the full phase space. These results
provide a valuable benchmark for ongoing EIC detector optimizations
and may also inform future studies involving high-granularity
calorimeters in other experiments at various facilities.