Speaker
Description
One of the major difficulties of particle reconstruction in calorimeters is the case of overlapping objects in the detector. This problem will become particularly concerning at the High-Luminosity LHC, where the increased luminosity will cause high levels of pile-up. High-granularity calorimeters, such as the future HGCal in the CMS endcap, allow us to perform Particle Flow (PF) reconstruction on particles with overlapping showers in the calorimeter. This task requires new algorithms that can adequately exploit the granular properties of future calorimeters.
We propose a Graph Neural Network architecture for a segmentation block that can split overlapping showers produced by two distinct electrons in a high-granularity electromagnetic calorimeter, and reconstruct the individual showers from each electron. In order to do so, it predicts, for each node, the fraction of its energy attributed to each individual shower. We introduce the optimisation work that was done on the model, in particular on the graph construction and convolution operations. We also show the separation efficiency of the model and demonstrate that it is at the state of the art, with significantly reduced resource consumption.