Energy measurement of hadron showers in the CMS HGCAL using Graph Neural Networks and a new physics search with photons at the CMS experiment

Abstract

The CMS experiment at the LHC has been rigorously testing the Standard Model in proton-proton collision data for more than a decade now. As the LHC progresses through Run-3 at a center-of-mass energy of 13.6 TeV, the CMS collaboration is also preparing for the High-Luminosity LHC (HL-LHC) phase to operate with more intense beams in the upcoming decade. To meet the challenges of high radiation doses and more particle flux, current CMS endcap calorimeters will be replaced by the High-Granularity Calorimeter (HGCAL), a state-of-the-art detector that features six million read-out channels capable of providing highly detailed 5-dimensional measurements: energy, position, and timing. These capabilities open new frontiers in the reconstruction of particle showers with unprecedented resolution. In this thesis, a novel dynamic reduction network (DRN) based on graphical neural networks (GNN) is used to significantly improve energy resolution of charged pions in the HGCAL. The improvements from the algorithm are validated on negatively charged pion data collected with the HGCAL beam test experiments at the CERN facilities. Further insight into the additional information learned by the GNN is obtained using GEANT4 based detector simulation. As the HGCAL approaches its preproduction phase, it is imperative to evaluate the efficacy of the finalized design, with a particular focus on the readout components, through both system tests and beam tests to guarantee operational integrity and seamless data transmission without loss. This thesis presents key results from software based studies of the HGCAL readout electronics and data handling and from the beam test of complete near-final HGCAL readout system during the August-September 2023 experiments at the CERN H4 beam test facility.

I also present a new methodology for searching for new physics produced via strong and electroweak supersymmetry scenarios in Run 2 data using soft photons as a probe. Lowering the photon momentum threshold increases the background tenfold and reduces signal sensitivity when using the jet multiplicity and MET as primary criteria based on previous searches. By reevaluating events with photon momentum and the scalar sum of all jets and photon momentum, signal sensitivity improves across strong and electroweakino productions.