Calibration of electrons and photons in the CMS ECAL with graph neural networks

The Compact Muon Solenoid (CMS) detector is a general-purpose detector on the energy frontier of particle physics at the CERN Large Hadron Collider (LHC). Products of proton-proton collisions at a center-of-mass energy of 13 TeV are reconstructed in the CMS detector to probe the standard model of particle physics and to search for processes beyond the standard model. The development of precision algorithms for this reconstruction is therefore a key objective in optimizing the precision of all CMS physics results. While machine learning techniques are now prevalent at CMS for these tasks, they have largely relied on high-level human-engineered input features. However, much of the disruptive impact of machine learning in other fields has been realized by bypassing human feature engineering and instead training deep learning algorithms on low-level data. We have developed a novel machine learning architecture based on dynamic graph neural networks which allows regression directly on low-level detector hits, and we have applied this model to the calibration of electron and photon energies in CMS. In this work we will discuss our new architecture and show its performance in predicting the electron and photon energies used in physics analyses at CMS.