The Optimal use of Segmentation for Sampling Calorimeters
Fernando Torales Acosta Bishnu Karki Piyush Karande Aaron Angerami Miguel Arratia Kenneth Barish Ryan Milton Sebastián Morán Benjamin Nachman Anshuman Sinha
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.