Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC (2012.06888v2)

Abstract: The performance is presented of the reconstruction and identification algorithms for electrons and photons with the CMS experiment at the LHC. The reported results are based on proton-proton collision data collected at a center-of-mass energy of 13 TeV and recorded in 2016-2018, corresponding to an integrated luminosity of 136 fb$^{-1}$. Results obtained from lead-lead collision data collected at $\sqrt{s_\mathrm{NN}} =$ 5.02 TeV are also presented. Innovative techniques are used to reconstruct the electron and photon signals in the detector and to optimize the energy resolution. Events with electrons and photons in the final state are used to measure the energy resolution and energy scale uncertainty in the recorded events. The measured energy resolution for electrons produced in Z boson decays in proton-proton collision data ranges from 2 to 5%, depending on electron pseudorapidity and energy loss through bremsstrahlung in the detector material. The energy scale in the same range of energies is measured with an uncertainty smaller than 0.1 (0.3)% in the barrel (endcap) region in proton-proton collisions and better than 1 (3)% in the barrel (endcap) region in heavy ion collisions. The timing resolution for electrons from Z boson decays with the full 2016-2018 proton-proton collision data set is measured to be 200 ps.

• The paper introduces innovative reconstruction techniques that integrate clustering, tracking, and particle-flow methods to enhance electron and photon identification.
• The paper employs regression and boosted decision tree analyses to correct ECAL energy measurements, reducing scale uncertainties to 0.1% in the barrel.
• The paper demonstrates robust trigger performance in both pp and PbPb collisions, ensuring efficient event selection under challenging conditions.

Overview of Electron and Photon Reconstruction in CMS at LHC

The paper presents a detailed examination of the algorithms developed for electron and photon reconstruction and identification used by the CMS experiment at the LHC during Run 2, leveraging data from $\Pp\Pp$ collisions. Addressing the technical challenges posed by intricate detection environments, the CMS experiment's electron and photon reconstruction methodologies reflect advancements aimed at optimizing data integrity in high-energy physics research.

CMS employs innovative clustering and tracking algorithms to manage the detection complexities associated with the high bremsstrahlung occurrence in the dense material before the electromagnetic calorimeter (ECAL). The electron and photon reconstruction integrates within the particle-flow (PF) framework and utilizes the Gaussian sum filter (GSF) tracking specifically designed for electrons. These strategies optimize the performance by capturing the energy spread associated with electromagnetic showering and conversion processes.

A significant methodological component is the application of regression techniques to refine energy measurements. Multivariate analyses, using boosted decision trees (BDTs), are employed to adjust for discrepancies in the ECAL's measured response caused by lateral and longitudinal shower leakages, modular gaps, and tracker-induced energy loss. This approach ensures that the corrected energy from the ECAL aligns closely with the true particle energy, which is paramount for precise particle identification and subsequent physics analyses.

Importantly, the paper includes robust calibration techniques utilizing $\PZ\to \Pe\Pe$ samples to improve the reliability of electron energy scale and resolution across various pseudorapidity regions. The tuning of energy scale corrections yields scale uncertainties as low as 0.1% in the barrel and less than 0.3% in the endcap regions, underscoring the high precision achieved in the CMS experiment's ECAL calibration effort.

Additionally, the paper discusses the performance of electron and photon triggers employed in the 2016–2018 timeframe, emphasizing the balance between high event selection efficiency and computational cost. Trigger algorithms are designed to swiftly process large datasets while maintaining the necessary fidelity to detect high-energy physics phenomena.

For lead-lead (PbPb) collisions, reconstruction methodologies were adapted to account for the elevated multiplicities and associated computational load, thus enabling credible particle detection even in the dense heavy-ion collision backdrop. The PbPb collision data further validate the adaptability and accuracy of CMS's reconstruction algorithms under varying conditions.

The paper concludes by illustrating the improved result fidelity in electron and photon kinematic measurements, contributing substantial enhancements to the CMS experiment's research capabilities across the LHC's productive years. These advancements represent a cornerstone for high precision in high-energy particle physics research, alongside a readiness to tackle new computational and material challenges in future collider experiments. The integration of refined reconstruction algorithms holds potential for a broader understanding of fundamental process dynamics at quantum levels.