Particle-flow reconstruction and global event description with the CMS detector (1706.04965v2)

Abstract: The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic tau decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8 TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions.

• The paper introduces an innovative particle-flow reconstruction algorithm that integrates multi-detector measurements for precise identification of particles in high-energy collisions.
• It significantly improves physics object reconstruction by delivering enhanced jet resolution, more accurate MET estimation, and optimized electron and muon identification.
• The study provides important insights and suggests detector upgrades to mitigate pileup challenges, paving the way for refined analyses in future high-luminosity collider experiments.

Overview of Particle-Flow Reconstruction and Global Event Description with the CMS Detector

This paper presents an in-depth account of the particle-flow (PF) reconstruction algorithm developed and used within the CMS detector at CERN's Large Hadron Collider (LHC). The PF reconstruction strategy enables the comprehensive identification and reconstruction of final-state particles resulting from proton-proton collisions, offering a more detailed global event description than traditional approaches.

Technical Implementation and Capabilities

The CMS detector's advantageous architecture, including a finely segmented tracker, a powerful magnetic field, and calorimeters with various granularities, makes it particularly suitable for the PF approach. This comprehensive setup allows for improved particle identification by correlating measurements from different subdetectors, encompassing the entire spectrum from charged and neutral hadrons to electrons, muons, and photons.

Central to this strategy is the holistic integration of tracking information, particularly effective for charged particle trajectory determination, with calorimetric energy deposits. This enables more accurate momentum and energy measurements even in the high multiplicity environments typical of LHC collisions.

The CMS collaboration has developed and deployed multiple iterations of a tracking algorithm to ensure efficient and accurate track finding, a cornerstone of the PF algorithm. This involves sequential improvements using track seeds from the silicon tracker to achieve high tracking efficiency while maintaining purity.

Improved Physics Object Reconstruction

The PF approach significantly enhances the performance metrics for all major physics objects. For instance, PF jets exhibit improved angular resolution, energy response, and flavor discrimination as compared to jets reconstructed using calorimeter-only information. The separation of charged hadrons and neutral components such as photons within jets is more precisely achieved, thereby reducing the uncertainties in jet energy and improving the identification of physics processes in data analyses.

In addition to jets, the PF technique also improves the reconstruction of missing transverse momentum (MET), a critical observable for processes characterized by escaping particles like neutrinos. By better managing the contribution of pileup, one of the significant challenges at the LHC, the PF algorithm enables more accurate MET estimates.

Another area of improvement lies in electron and muon identification, where the PF approach optimizes the balance between efficiency and fake rate. The inclusion of tracker-based seeding for electron reconstruction complements the calorimeter-based methods, capturing a wider set of electrons, particularly those in jets or with lower $p_T$.

Practical and Theoretical Implications

Practically, the successful deployment of PF at CMS has provided tangible benefits in the processing and analysis of collision data, contributing to the precision measurement of known processes and the search for new physics. The detailed event description acknowledges the complex nature of final states and enhances the reliability of high-level physics conclusions drawn from LHC experiments.

Theoretically, the paper suggests that PF reconstruction methodologies are a promising avenue for future collider experiments. Potential upgrades to CMS, incorporating more granular detectors and improved readout systems, aim to further refine PF reconstruction in anticipation of higher luminosity scenarios at the LHC.

Future Developments

Building on the achievements outlined, future detector upgrades promise to extend the capabilities and robustness of PF methods. Developments such as enhanced granularity in calorimetry and integrated tracking triggers are expected to improve the precision of particle-level measurements and extend PF applicability to the more challenging conditions expected in upcoming high-luminosity LHC runs.

In conclusion, this work by the CMS collaboration underscores the efficacy and transformative impact of particle-flow reconstruction on modern particle physics experiments, setting a strong precedent and framework for future advancements in this domain.