Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at sqrt(s) = 8 TeV (1502.02701v2)

Abstract: The performance and strategies used in electron reconstruction and selection at CMS are presented based on data corresponding to an integrated luminosity of 19.7 inverse femtobarns, collected in proton-proton collisions at sqrt(s) = 8 TeV at the CERN LHC. The paper focuses on prompt isolated electrons with transverse momenta ranging from about 5 to a few 100 GeV. A detailed description is given of the algorithms used to cluster energy in the electromagnetic calorimeter and to reconstruct electron trajectories in the tracker. The electron momentum is estimated by combining the energy measurement in the calorimeter with the momentum measurement in the tracker. Benchmark selection criteria are presented, and their performances assessed using Z, Upsilon, and J/psi decays into electron-positron pairs. The spectra of the observables relevant to electron reconstruction and selection as well as their global efficiencies are well reproduced by Monte Carlo simulations. The momentum scale is calibrated with an uncertainty smaller than 0.3%. The momentum resolution for electrons produced in Z boson decays ranges from 1.7 to 4.5%, depending on electron pseudorapidity and energy loss through bremsstrahlung in the detector material.

• The paper presents a detailed evaluation of electron reconstruction techniques in high-energy pp collisions at 8 TeV with 19.7 fb⁻¹ of data.
• It demonstrates the use of advanced methods like the Gaussian Sum Filter and MVA-based selection to achieve over 85% efficiency for electrons above 10 GeV.
• The analysis compares data with simulations, achieving momentum resolution between 1.7% and 4.5%, and highlights areas for future algorithmic improvements.

Overview of Electron Reconstruction and Selection at CMS

The paper provides a comprehensive evaluation of the performance of electron reconstruction and selection techniques implemented with the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC), operating at a center-of-mass energy of 8 TeV. It analyzes data corresponding to an integrated luminosity of 19.7 fb⁻¹, focusing on the strategies used to reconstruct and select electrons amidst high-energy proton-proton collisions.

Key Components of Electron Reconstruction

A central focus of this analysis is the efficiency and accuracy required for electron reconstruction, which is vital for a multitude of analyses in particle physics, including investigations into the Standard Model, the Higgs boson, and searches for physics beyond the Standard Model.

1. Clustering and Tracking Algorithms: The paper details the algorithms deployed to associate track information from the silicon detector with clustered energy deposits in the electromagnetic calorimeter (ECAL). Notably, the Gaussian Sum Filter (GSF) algorithm plays a crucial role in accommodating the challenge posed by bremsstrahlung losses along the electron’s path.
2. Momentum Calibration: An emphasis is placed on combining energy measurements from the ECAL with momentum data from the tracker to estimate electron momentum. The momentum scale is calibrated to an uncertainty below 0.3% for electrons with transverse momentum (p_T) ranging from 7 to 70 GeV.
3. Simulations and Data Matching: The performance metrics, in terms of resolution and scale accuracy, are scrutinized through a detailed comparison between real experimental data and Monte Carlo simulations. This includes adjusting for discrepancies, particularly in the material budget represented in simulation versus data.

Selection Criteria and Algorithms

In parallel with reconstruction, the selection of electrons is articulated through several methodologies designed for optimal efficiency and minimal misidentification.

• Sequential Selection: This approach applies a series of threshold criteria across multiple discriminating variables, including the spatial match between cluster and tracks, energy isolation, and specific calorimetric and tracking measurements.
• MVA-based Selection: Machine Learning methods are adopted, specifically Boosted Decision Trees (BDTs), to enhance signal-to-background discrimination. These are trained on an extensive set of input variables, capitalizing on subtle correlations that might elude a traditional sequential approach.

Performance Results and Implications

The results underscore the ability of CMS to achieve electron reconstruction efficiencies exceeding 85% for energies above 10 GeV, with a momentum resolution ranging from 1.7% to 4.5%, contingent on factors such as pseudorapidity and bremsstrahlung characteristics. The paper highlights several benchmark scenarios, with simulation generally matching well to observed data, albeit necessity for fine-tuning, especially at low p_T.

Future Directions and Considerations

As methodologies and data handling processes continue to evolve, this comprehensive performance evaluation provides a benchmark for subsequent investigative and analytical endeavors at LHC. Continuing advancements in algorithmic approaches, potentially leveraging deeper machine learning architectures or enhanced simulation detail, could further elevate the precision of electron measurements, critical to probing even subtler phenomena in particle physics. Furthermore, understanding and mitigating any discrepancies between observed data and theoretical simulations remains a continuous endeavor, essential for validating models fundamental to theoretical physics.

In conclusion, the paper forms a strong foundation for discussing the challenges and achievements in electron reconstruction and selection within a large-scale high-energy physics experiment, highlighting both the technical sophistication of current practices and the potential for future enhancements.