Speaker
Description
We present an end-to-end reconstruction algorithm for highly granular calorimeters that includes track information to aid the reconstruction of charged particles. The algorithm starts from calorimeter hits and reconstructed tracks, and outputs a coordinate transformation in which all shower objects are well separated from each other, and in which clustering becomes trivial. Shower properties such as particle ID and energy are predicted from representative points within showers. This is achieved using an extended version of the object condensation loss, a graph segmentation technique that allows the clustering of a variable number of showers in every event while simultaneously performing regression and classification tasks. The backbone is an architecture based on a newly-developed translation-equivariant version of GravNet layers. These dynamically build learnable graphs from input data to exchange information along their edges. The model is trained on data from a simulated detector that matches the complexity of the CMS high-granularity calorimeter (HGCAL), for which it can be retrained in the future.
